diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 5727a426..4faf84a6 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -10,7 +10,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ['3.9', '3.10', '3.11']
+        python-version: ['3.11', '3.12', '3.13']
 
     steps:
     - uses: actions/checkout@v2
diff --git a/.gitignore b/.gitignore
index ea604a61..55ebb440 100644
--- a/.gitignore
+++ b/.gitignore
@@ -36,3 +36,4 @@ nosetests.xml
 
 .idea
 notebooks/.ipynb_checkpoints
+/venv
\ No newline at end of file
diff --git a/hpsklearn/components/__init__.py b/hpsklearn/components/__init__.py
index 9a529195..b9c18469 100644
--- a/hpsklearn/components/__init__.py
+++ b/hpsklearn/components/__init__.py
@@ -147,7 +147,9 @@
     lightgbm_regression
 
 from .feature_extraction import \
-    tfidf
+    tfidf_vectorizer, \
+    hashing_vectorizer, \
+    count_vectorizer
 
 from .decomposition import pca
 
@@ -253,7 +255,11 @@ def any_text_preprocessing(name):
     """
     Generic pre-processing appropriate for text data
     """
-    return hp.choice(name, [[tfidf(name + '.tfidf')]])
+    return hp.choice(name, [
+        [tfidf_vectorizer(name + ".tfidf")],
+        [hashing_vectorizer(name + ".hashing")],
+        [count_vectorizer(name + ".count")],
+    ])
 
 
 # Legacy any pre-processing as proposed in #137
@@ -423,7 +429,9 @@ def all_preprocessing(name):
         [polynomial_features(name + ".polynomial_features")],
         [spline_transformer(name + ".spline_transformer")],
         [k_bins_discretizer(name + ".k_bins_discretizer")],
-        [tfidf(name + ".tfidf")],
+        [tfidf_vectorizer(name + ".tfidf")],
+        [hashing_vectorizer(name + ".hashing")],
+        [count_vectorizer(name + ".count")],
         [pca(name + ".pca")],
         [ts_lagselector(name + ".ts_lagselector")],
         [colkmeans(name + ".colkmeans")],
diff --git a/hpsklearn/components/cluster/_kmeans.py b/hpsklearn/components/cluster/_kmeans.py
index 1365fdc4..3780c258 100644
--- a/hpsklearn/components/cluster/_kmeans.py
+++ b/hpsklearn/components/cluster/_kmeans.py
@@ -44,7 +44,8 @@ def _kmeans_hp_space(
         n_clusters: typing.Union[int, Apply] = None,
         init: typing.Union[str, callable, npt.ArrayLike, Apply] = None,
         verbose: int = 0,
-        random_state=None
+        random_state=None,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -55,7 +56,8 @@ def _kmeans_hp_space(
         n_clusters=_kmeans_n_clusters(name_func("n_clusters")) if n_clusters is None else n_clusters,
         init=_kmeans_init(name_func("init")) if init is None else init,
         verbose=verbose,
-        random_state=_kmeans_random_state(name_func("random_state")) if random_state is None else random_state
+        random_state=_kmeans_random_state(name_func("random_state")) if random_state is None else random_state,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/compose/_target.py b/hpsklearn/components/compose/_target.py
index 47122eee..1b7ff15f 100644
--- a/hpsklearn/components/compose/_target.py
+++ b/hpsklearn/components/compose/_target.py
@@ -13,7 +13,8 @@ def transformed_target_regressor(name: str,
                                  transformer: object = None,
                                  func: callable = None,
                                  inverse_func: callable = None,
-                                 check_inverse: bool = True):
+                                 check_inverse: bool = True,
+                                 **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.compose.TransformedTargetRegressor model.
@@ -36,6 +37,7 @@ def _name(msg):
         transformer=transformer,
         func=func,
         inverse_func=inverse_func,
-        check_inverse=check_inverse
+        check_inverse=check_inverse,
+        **kwargs
     )
     return scope.sklearn_TransformedTargetRegressor(**hp_space)
diff --git a/hpsklearn/components/covariance/_elliptic_envelope.py b/hpsklearn/components/covariance/_elliptic_envelope.py
index 99d736b8..c1033283 100644
--- a/hpsklearn/components/covariance/_elliptic_envelope.py
+++ b/hpsklearn/components/covariance/_elliptic_envelope.py
@@ -16,7 +16,8 @@ def elliptic_envelope(name: str,
                       assume_centered: bool = False,
                       support_fraction: typing.Union[float, Apply] = None,
                       contamination: typing.Union[float, Apply] = 0.1,
-                      random_state=None):
+                      random_state=None,
+                      **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.covariance.EllipticEnvelope model.
@@ -39,6 +40,7 @@ def _name(msg):
         support_fraction=hp.uniform(_name("support_fraction"), 0.05, 0.95)
         if support_fraction is None else support_fraction,
         contamination=hp.uniform(_name("contamination"), 0.0, 0.3) if contamination is None else contamination,
-        random_state=hp.randint(_name("random_state"), 5) if random_state is None else random_state
+        random_state=hp.randint(_name("random_state"), 5) if random_state is None else random_state,
+        **kwargs
     )
     return scope.sklearn_EllipticEnvelope(**hp_space)
diff --git a/hpsklearn/components/cross_decomposition/_pls.py b/hpsklearn/components/cross_decomposition/_pls.py
index bdad8618..e4649957 100644
--- a/hpsklearn/components/cross_decomposition/_pls.py
+++ b/hpsklearn/components/cross_decomposition/_pls.py
@@ -51,7 +51,8 @@ def _pls_hp_space(
         scale: bool = True,
         max_iter: typing.Union[int, Apply] = None,
         tol: typing.Union[float, Apply] = None,
-        copy: bool = True
+        copy: bool = True,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -64,7 +65,8 @@ def _pls_hp_space(
         scale=scale,
         max_iter=_pls_max_iter(name_func("max_iter")) if max_iter is None else max_iter,
         tol=_pls_tol(name_func("tol")) if tol is None else tol,
-        copy=copy
+        copy=copy,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/discriminant_analysis.py b/hpsklearn/components/discriminant_analysis.py
index 0755e630..216f7ca4 100644
--- a/hpsklearn/components/discriminant_analysis.py
+++ b/hpsklearn/components/discriminant_analysis.py
@@ -30,7 +30,8 @@ def _discriminant_analysis_hp_space(
         name_func,
         priors: npt.ArrayLike = None,
         store_covariance: bool = False,
-        tol: float = None
+        tol: float = None,
+        **kwargs
 ):
     """
     Common hyper parameter search space
@@ -40,7 +41,8 @@ def _discriminant_analysis_hp_space(
     hp_space = dict(
         priors=priors,
         store_covariance=store_covariance,
-        tol=_discriminant_analysis_tol(name_func("tol")) if tol is None else tol
+        tol=_discriminant_analysis_tol(name_func("tol")) if tol is None else tol,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/dummy.py b/hpsklearn/components/dummy.py
index 15b7717b..d2a8ab03 100644
--- a/hpsklearn/components/dummy.py
+++ b/hpsklearn/components/dummy.py
@@ -26,7 +26,8 @@ def sklearn_DummyRegressor(*args, **kwargs):
 def dummy_classifier(name: str,
                      strategy: typing.Union[str, Apply] = None,
                      random_state=None,
-                     constant: typing.Union[int, str, npt.ArrayLike] = None):
+                     constant: typing.Union[int, str, npt.ArrayLike] = None,
+                     **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.dummy.DummyClassifier model.
@@ -45,7 +46,8 @@ def _name(msg):
         strategy=hp.choice(_name("strategy"), ["stratified", "most_frequent", "prior", "uniform"])
         if strategy is None else strategy,
         random_state=hp.randint(_name("random_state"), 5) if random_state is None else random_state,
-        constant=constant
+        constant=constant,
+        **kwargs
     )
     return scope.sklearn_DummyClassifier(**hp_space)
 
@@ -60,7 +62,8 @@ def _name(msg):
 def dummy_regressor(name: str,
                     strategy: typing.Union[str, Apply] = None,
                     constant: typing.Union[int, str, npt.ArrayLike] = None,
-                    quantile: float = None):
+                    quantile: float = None,
+                    **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.dummy.DummyRegressor model.
@@ -78,6 +81,7 @@ def _name(msg):
     hp_space = dict(
         strategy=hp.choice(_name("strategy"), ["mean", "median"]) if strategy is None else strategy,
         constant=constant,
-        quantile=quantile
+        quantile=quantile,
+        **kwargs
     )
     return scope.sklearn_DummyRegressor(**hp_space)
diff --git a/hpsklearn/components/ensemble/_bagging.py b/hpsklearn/components/ensemble/_bagging.py
index 0fc8ff82..319f2b76 100644
--- a/hpsklearn/components/ensemble/_bagging.py
+++ b/hpsklearn/components/ensemble/_bagging.py
@@ -93,6 +93,7 @@ def _bagging_hp_space(
         n_jobs: int = 1,
         random_state=None,
         verbose: int = False,
+        **kwargs,
 ):
     """
     Hyper parameter search space for
@@ -112,6 +113,7 @@ def _bagging_hp_space(
         n_jobs=n_jobs,
         random_state=_bagging_random_state(name_func("random_state")) if random_state is None else random_state,
         verbose=verbose,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/ensemble/_forest.py b/hpsklearn/components/ensemble/_forest.py
index 5349b80b..bb14c74d 100644
--- a/hpsklearn/components/ensemble/_forest.py
+++ b/hpsklearn/components/ensemble/_forest.py
@@ -190,7 +190,8 @@ def _forest_hp_space(
         verbose: int = False,
         warm_start: bool = False,
         ccp_alpha: float = 0.0,
-        max_samples: float = None
+        max_samples: float = None,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -224,7 +225,8 @@ def _forest_hp_space(
         verbose=verbose,
         warm_start=warm_start,
         ccp_alpha=ccp_alpha,
-        max_samples=max_samples
+        max_samples=max_samples,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/ensemble/_gb.py b/hpsklearn/components/ensemble/_gb.py
index 92697ffc..28069de3 100644
--- a/hpsklearn/components/ensemble/_gb.py
+++ b/hpsklearn/components/ensemble/_gb.py
@@ -174,7 +174,8 @@ def _gb_hp_space(
         validation_fraction: float = 0.1,
         n_iter_no_change: int = None,
         tol: float = 1e-4,
-        ccp_alpha: float = 0.0
+        ccp_alpha: float = 0.0,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -206,6 +207,7 @@ def _gb_hp_space(
         n_iter_no_change=n_iter_no_change,
         tol=tol,
         ccp_alpha=ccp_alpha,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/ensemble/_hist_gradient_boosting.py b/hpsklearn/components/ensemble/_hist_gradient_boosting.py
index 4e4c761f..ba2aeaa0 100644
--- a/hpsklearn/components/ensemble/_hist_gradient_boosting.py
+++ b/hpsklearn/components/ensemble/_hist_gradient_boosting.py
@@ -73,6 +73,13 @@ def _hist_gradient_boosting_random_state(name: str):
     return hp.randint(name, 5)
 
 
+def _hist_gradient_boosting_max_features(name: str):
+    """
+    Declaration search space 'max_features' parameter
+    """
+    return hp.uniform(name + ".frac", 0.5, 1.)
+
+
 @validate(params=["max_bins"],
           validation_test=lambda param: not isinstance(param, int) or 0 < param <= 255,
           msg="Invalid parameter '%s' with value '%s'. "
@@ -89,6 +96,7 @@ def _hist_gradient_boosting_hp_space(
         max_depth: typing.Union[int, Apply] = "Undefined",
         min_samples_leaf: typing.Union[int, Apply] = None,
         l2_regularization: float = 0,
+        max_features: typing.Union[float, Apply] = None,
         max_bins: int = 255,
         categorical_features: npt.ArrayLike = None,
         monotonic_cst: npt.ArrayLike = None,
@@ -99,7 +107,8 @@ def _hist_gradient_boosting_hp_space(
         n_iter_no_change: int = 10,
         tol: float = 1e-7,
         verbose: int = False,
-        random_state=None
+        random_state=None,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -123,6 +132,8 @@ def _hist_gradient_boosting_hp_space(
         min_samples_leaf=_hist_gradient_boosting_min_samples_leaf(name_func("min_samples_leaf"))
         if min_samples_leaf is None else min_samples_leaf,
         l2_regularization=l2_regularization,
+        max_features=_hist_gradient_boosting_max_features(name_func("max_features"))
+        if max_features is None else max_features,
         max_bins=max_bins,
         categorical_features=categorical_features,
         monotonic_cst=monotonic_cst,
@@ -134,7 +145,8 @@ def _hist_gradient_boosting_hp_space(
         tol=tol,
         verbose=verbose,
         random_state=_hist_gradient_boosting_random_state(name_func("random_state"))
-        if random_state is None else random_state
+        if random_state is None else random_state,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/ensemble/_iforest.py b/hpsklearn/components/ensemble/_iforest.py
index afb60863..f0ea84ef 100644
--- a/hpsklearn/components/ensemble/_iforest.py
+++ b/hpsklearn/components/ensemble/_iforest.py
@@ -59,7 +59,8 @@ def _iforest_hp_space(
         n_jobs: int = 1,
         random_state=None,
         verbose: int = False,
-        warm_start: bool = False
+        warm_start: bool = False,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -74,7 +75,8 @@ def _iforest_hp_space(
         n_jobs=n_jobs,
         random_state=_iforest_random_state(name_func("random_state")) if random_state is None else random_state,
         verbose=verbose,
-        warm_start=warm_start
+        warm_start=warm_start,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/ensemble/_weight_boosting.py b/hpsklearn/components/ensemble/_weight_boosting.py
index 95ae6e84..f411b1d0 100644
--- a/hpsklearn/components/ensemble/_weight_boosting.py
+++ b/hpsklearn/components/ensemble/_weight_boosting.py
@@ -32,13 +32,6 @@ def _weight_boosting_learning_rate(name: str):
     return hp.lognormal(name, np.log(0.01), np.log(10.0))
 
 
-def _weight_boosting_algorithm(name: str):
-    """
-    Declaration search space 'algorithm' parameter
-    """
-    return hp.choice(name, ["SAMME", "SAMME.R"])
-
-
 def _weight_boosting_loss(name: str):
     """
     Declaration search space 'loss' parameter
@@ -57,11 +50,12 @@ def _weight_boosting_random_state(name: str):
           validation_test=lambda param: not isinstance(param, float) or param > 0,
           msg="Invalid parameter '%s' with value '%s'. Parameter value must be non-negative and greater than 0.")
 def _weight_boosting_hp_space(
-        name_func,
-        estimator=None,
-        n_estimators: typing.Union[int, Apply] = None,
-        learning_rate: typing.Union[float, Apply] = None,
-        random_state=None
+    name_func,
+    estimator=None,
+    n_estimators: typing.Union[int, Apply] = None,
+    learning_rate: typing.Union[float, Apply] = None,
+    random_state=None,
+    **kwargs
 ):
     """
     Hyper parameter search space for
@@ -74,18 +68,18 @@ def _weight_boosting_hp_space(
         learning_rate=_weight_boosting_learning_rate(name_func("learning_rate"))
         if learning_rate is None else learning_rate,
         random_state=_weight_boosting_random_state(name_func("random_state")) if random_state is None else random_state,
+        **kwargs
     )
     return hp_space
 
 
-def ada_boost_classifier(name: str, algorithm: typing.Union[str, Apply] = None, **kwargs):
+def ada_boost_classifier(name: str, **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.ensemble.AdaBoostClassifier model.
 
     Args:
         name: name | str
-        algorithm: choose 'SAMME' or 'SAMME.R' | str
 
     See help(hpsklearn.components.ensemble._weight_boosting._weight_boosting_hp_space)
     for info on additional available AdaBoost arguments.
@@ -95,7 +89,6 @@ def _name(msg):
         return f"{name}.ada_boost_{msg}"
 
     hp_space = _weight_boosting_hp_space(_name, **kwargs)
-    hp_space["algorithm"] = _weight_boosting_algorithm(_name("algorithm")) if algorithm is None else algorithm
 
     return scope.sklearn_AdaBoostClassifier(**hp_space)
 
diff --git a/hpsklearn/components/feature_extraction/__init__.py b/hpsklearn/components/feature_extraction/__init__.py
index 37e57a41..a51c8eba 100644
--- a/hpsklearn/components/feature_extraction/__init__.py
+++ b/hpsklearn/components/feature_extraction/__init__.py
@@ -1 +1,4 @@
-from .text import tfidf
+from .text import \
+    tfidf_vectorizer, \
+    hashing_vectorizer, \
+    count_vectorizer
diff --git a/hpsklearn/components/feature_extraction/text.py b/hpsklearn/components/feature_extraction/text.py
index c09bb26c..eeeca39e 100644
--- a/hpsklearn/components/feature_extraction/text.py
+++ b/hpsklearn/components/feature_extraction/text.py
@@ -12,61 +12,190 @@ def sklearn_TfidfVectorizer(*args, **kwargs):
     return feature_extraction.text.TfidfVectorizer(*args, **kwargs)
 
 
+@scope.define
+def sklearn_HashingVectorizer(*args, **kwargs):
+    return feature_extraction.text.HashingVectorizer(*args, **kwargs)
+
+
+@scope.define
+def sklearn_CountVectorizer(*args, **kwargs):
+    return feature_extraction.text.CountVectorizer(*args, **kwargs)
+
+
+def _text_analyzer(name: str):
+    """
+    Declaration search space 'analyzer' parameter
+    """
+    return hp.choice(name, ["word", "char", "char_wb"])
+
+
+def _text_stop_words(name: str):
+    """
+    Declaration search space 'stop_words' parameter
+    """
+    return hp.choice(name, ["english", None])
+
+
+def _text_lowercase(name: str):
+    """
+    Declaration search space 'lowercase' parameter
+    """
+    return hp.choice(name, [True, False])
+
+
+def _text_max_df(name: str):
+    """
+    Declaration search space 'max_df' parameter
+    """
+    return hp.uniform(name, 0.7, 1.0)
+
+
+def _text_min_df(name: str):
+    """
+    Declaration search space 'min_df' parameter
+    """
+    return hp.uniform(name, 0.0, 0.2)
+
+
+def _text_max_features(name: str):
+    """
+    Declaration search space 'max_features' parameter
+    """
+    return hp.pchoice(name, [
+        (0.4, scope.int(hp.uniform(name + ".int", 1, 5))),
+        (0.6, None)
+    ])
+
+
+def _text_binary(name: str):
+    """
+    Declaration search space 'binary' parameter
+    """
+    return hp.choice(name, [True, False])
+
+
+def _text_max_ngram(name: str):
+    """
+    Declaration maximum range for 'ngram_range' parameter
+    """
+    return scope.int(hp.quniform(name, 1, 4, 1))
+
+
+def _text_norm(name: str):
+    """
+    Declaration search space 'norm' parameter
+    """
+    return hp.choice(name, ["l1", "l2"])
+
+
 @validate(params=["analyzer"],
           validation_test=lambda param: not isinstance(param, str) or param in ["word", "char", "char_wb"],
           msg="Invalid parameter '%s' with value '%s'. Value must be one of 'word', 'char', 'char_wb'.")
+def _text_hp_space(
+        name_func,
+        analyzer: typing.Union[str, callable, Apply] = None,
+        stop_words: typing.Union[str, list, Apply] = None,
+        lowercase: typing.Union[bool, Apply] = None,
+        binary: typing.Union[bool, Apply] = None,
+        ngram_range: tuple = None,
+        **kwargs,
+):
+    """
+    Hyper parameter search space for
+     tfidf vectorizer
+     hashing vectorizer
+     count vectorizer
+    """
+    hp_space = dict(
+        analyzer=_text_analyzer(name_func("analyzer")) if analyzer is None else analyzer,
+        stop_words=_text_stop_words(name_func("stop_words")) if stop_words is None else stop_words,
+        lowercase=_text_lowercase(name_func("lowercase")) if lowercase is None else lowercase,
+        binary=_text_binary(name_func("binary")) if binary is None else binary,
+        ngram_range=(1, _text_max_ngram(name_func("ngram_range"))) if ngram_range is None else ngram_range,
+        **kwargs,
+    )
+    return hp_space
+
+
 @validate(params=["norm"],
           validation_test=lambda param: not isinstance(param, str) or param in ["l1", "l2"],
           msg="Invalid parameter '%s' with value '%s'. Value must be one of 'l1', 'l2'.")
-def tfidf(name: str,
-          analyzer: typing.Union[str, callable, Apply] = None,
-          ngram_range: tuple = None,
-          stop_words: typing.Union[str, list, Apply] = None,
-          lowercase: typing.Union[bool, Apply] = None,
-          max_df: typing.Union[float, int, Apply] = 1.0,
-          min_df: typing.Union[float, int, Apply] = 1,
-          max_features: int = None,
-          binary: typing.Union[bool, Apply] = None,
-          norm: typing.Union[str, Apply] = None,
-          use_idf: bool = False,
-          smooth_idf: bool = False,
-          sublinear_tf: bool = False):
+def tfidf_vectorizer(
+        name: str,
+        max_df: typing.Union[float, int, Apply] = 1.0,
+        min_df: typing.Union[float, int, Apply] = 1,
+        max_features: typing.Union[int, Apply] = None,
+        norm: typing.Union[str, Apply] = None,
+        **kwargs,
+):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.feature_extraction.text.TfidfVectorizer transformer.
 
     Args:
         name: name | str
-        analyzer: features made of word or char n-grams | str, callable
-        ngram_range: lower and upper boundary of n values | tuple
-        stop_words: stop words | str, list
-        lowercase: convert all characters to lowercase | bool
         max_df: upper bound document frequency | float
         min_df: lower bound document frequency | float
-        max_features: max number of features | int
-        binary: set non-zero term counts to 1 | bool
+        max_features: maximum features to consider | int
         norm: 'l1', 'l2' or None | str
-        use_idf: enable inverse-document-frequency reweighting | bool
-        smooth_idf: smooth idf weights by adding one to document frequencies | bool
-        sublinear_tf: apply sublinear tf scaling | bool
     """
     def _name(msg):
         return f"{name}.tfidf_vectorizer_{msg}"
 
-    max_ngram = scope.int(hp.quniform(_name("max_ngram"), 1, 4, 1))
-
-    rval = scope.sklearn_TfidfVectorizer(
-        analyzer=hp.choice(_name("analyzer"), ["word", "char", "char_wb"]) if analyzer is None else analyzer,
-        stop_words=hp.choice(_name("stop_words"), ["english", None]) if stop_words is None else stop_words,
-        lowercase=hp.choice(_name("lowercase"), [True, False]) if lowercase is None else lowercase,
-        max_df=max_df,
-        min_df=min_df,
-        max_features=max_features,
-        binary=hp.choice(_name("binary"), [True, False]) if binary is None else binary,
-        ngram_range=(1, max_ngram) if ngram_range is None else ngram_range,
-        norm=norm,
-        use_idf=use_idf,
-        smooth_idf=smooth_idf,
-        sublinear_tf=sublinear_tf,
-    )
-    return rval
+    hp_space = _text_hp_space(_name, **kwargs)
+    hp_space["max_df"] = _text_max_df(_name("max_df")) if max_df is None else max_df
+    hp_space["min_df"] = _text_min_df(_name("min_df")) if min_df is None else min_df
+    hp_space["norm"] = _text_norm(_name("norm")) if norm is None else norm
+    hp_space["max_features"] = _text_max_features(_name("max_features")) \
+        if max_features is not None else max_features
+
+    return scope.sklearn_TfidfVectorizer(**hp_space)
+
+
+@validate(params=["norm"],
+          validation_test=lambda param: not isinstance(param, str) or param in ["l1", "l2"],
+          msg="Invalid parameter '%s' with value '%s'. Value must be one of 'l1', 'l2'.")
+def hashing_vectorizer(name: str, norm: typing.Union[str, Apply] = None, **kwargs):
+    """
+    Return a pyll graph with hyperparameters that will construct
+    a sklearn.feature_extraction.text.HashingVectorizer transformer.
+
+    Args:
+        name: name | str
+        norm: 'l1', 'l2' or None | str
+    """
+    def _name(msg):
+        return f"{name}.hashing_vectorizer_{msg}"
+
+    hp_space = _text_hp_space(_name, **kwargs)
+    hp_space["norm"] = _text_norm(_name("norm")) if norm is None else norm
+
+    return scope.sklearn_HashingVectorizer(**hp_space)
+
+
+def count_vectorizer(
+        name: str,
+        max_df: typing.Union[float, int, Apply] = 1.0,
+        min_df: typing.Union[float, int, Apply] = 1,
+        max_features: typing.Union[int, Apply] = None,
+        **kwargs,
+):
+    """
+    Return a pyll graph with hyperparameters that will construct
+    a sklearn.feature_extraction.text.CountVectorizer transformer.
+
+    Args:
+        max_df: upper bound document frequency | float
+        min_df: lower bound document frequency | float
+        max_features: maximum features to consider | int
+    """
+    def _name(msg):
+        return f"{name}.count_vectorizer_{msg}"
+
+    hp_space = _text_hp_space(_name, **kwargs)
+    hp_space["max_df"] = _text_max_df(_name("max_df")) if max_df is None else max_df
+    hp_space["min_df"] = _text_min_df(_name("min_df")) if min_df is None else min_df
+    hp_space["max_features"] = _text_max_features(_name("max_features")) \
+        if max_features is not None else max_features
+
+    return scope.sklearn_CountVectorizer(**hp_space)
diff --git a/hpsklearn/components/gaussian_process/_gpc.py b/hpsklearn/components/gaussian_process/_gpc.py
index a80cbb59..53d52b2d 100644
--- a/hpsklearn/components/gaussian_process/_gpc.py
+++ b/hpsklearn/components/gaussian_process/_gpc.py
@@ -27,7 +27,8 @@ def gaussian_process_classifier(name: str,
                                 copy_X_train: bool = True,
                                 random_state=None,
                                 multi_class: typing.Union[str, Apply] = None,
-                                n_jobs: int = 1):
+                                n_jobs: int = 1,
+                                **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.gaussian_process.GaussianProcessClassifier model.
@@ -61,6 +62,7 @@ def _name(msg):
         random_state=hp.randint(_name("random_state"), 5) if random_state is None else random_state,
         multi_class=hp.choice(_name("multi_class"), ["one_vs_rest", "one_vs_one"])
         if multi_class is None else multi_class,
-        n_jobs=n_jobs
+        n_jobs=n_jobs,
+        **kwargs
     )
     return scope.sklearn_GaussianProcessClassifier(**hp_space)
diff --git a/hpsklearn/components/gaussian_process/_gpr.py b/hpsklearn/components/gaussian_process/_gpr.py
index 880c8d87..8319ed4f 100644
--- a/hpsklearn/components/gaussian_process/_gpr.py
+++ b/hpsklearn/components/gaussian_process/_gpr.py
@@ -23,7 +23,8 @@ def gaussian_process_regressor(name: str,
                                n_restarts_optimizer: typing.Union[int, Apply] = None,
                                normalize_y: bool = False,
                                copy_X_train: bool = True,
-                               random_state=None):
+                               random_state=None,
+                               **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.gaussian_process.GaussianProcessRegressor model.
@@ -52,5 +53,6 @@ def _name(msg):
         normalize_y=normalize_y,
         copy_X_train=copy_X_train,
         random_state=hp.randint(_name("random_state"), 5) if random_state is None else random_state,
+        **kwargs
     )
     return scope.sklearn_GaussianProcessRegressor(**hp_space)
diff --git a/hpsklearn/components/kernel_ridge.py b/hpsklearn/components/kernel_ridge.py
index 55102e13..a1d44ddd 100644
--- a/hpsklearn/components/kernel_ridge.py
+++ b/hpsklearn/components/kernel_ridge.py
@@ -25,7 +25,8 @@ def hp_sklearn_kernel_ridge(name: str,
                             gamma: typing.Union[float, Apply] = None,
                             degree: typing.Union[float, Apply] = None,
                             coef0: typing.Union[float, Apply] = None,
-                            kernel_params: map = None):
+                            kernel_params: map = None,
+                            **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.kernel_ridge.KernelRidge model.
@@ -51,5 +52,6 @@ def _name(msg):
         degree=scope.int(hp.uniform(_name("degree"), 1, 7)) if degree is None else degree,
         coef0=hp.uniform(_name("coef0"), 0.0, 1.0) if coef0 is None else coef0,
         kernel_params=kernel_params,
+        **kwargs
     )
     return scope.sklearn_KernelRidge(**hp_space)
diff --git a/hpsklearn/components/lightgbm.py b/hpsklearn/components/lightgbm.py
index 26f91d3f..1d1009d6 100644
--- a/hpsklearn/components/lightgbm.py
+++ b/hpsklearn/components/lightgbm.py
@@ -54,13 +54,6 @@ def _lightgbm_n_estimators(name):
     return scope.int(hp.quniform(name, 100, 6000, 200))
 
 
-def _lightgbm_gamma(name):
-    """
-    Declaration search space 'gamma' parameter
-    """
-    return hp.loguniform(name, np.log(0.0001), np.log(5)) - 0.0001
-
-
 def _lightgbm_min_child_weight(name):
     """
     Declaration search space 'min_child_weight' parameter
@@ -82,13 +75,6 @@ def _lightgbm_colsample_bytree(name):
     return hp.uniform(name, 0.5, 1)
 
 
-def _lightgbm_colsample_bylevel(name):
-    """
-    Declaration search space 'colsample_bylevel' parameter
-    """
-    return hp.uniform(name, 0.5, 1)
-
-
 def _lightgbm_reg_alpha(name):
     """
     Declaration search space 'reg_alpha' parameter
@@ -134,7 +120,8 @@ def _lightgbm_hp_space(
         reg_lambda: typing.Union[float, Apply] = None,
         boosting_type: typing.Union[str, Apply] = None,
         scale_pos_weight: float = 1,
-        random_state=None):
+        random_state=None,
+        **kwargs):
     """
     Hyper parameter search space for
      lightgbm classifier
@@ -157,7 +144,8 @@ def _lightgbm_hp_space(
         reg_lambda=_lightgbm_reg_lambda(name_func("reg_lambda")) if reg_lambda is None else reg_lambda,
         boosting_type=_lightgbm_boosting_type(name_func("boosting_type")) if boosting_type is None else boosting_type,
         scale_pos_weight=scale_pos_weight,
-        seed=_lightgbm_random_state(name_func("random_state")) if random_state is None else random_state
+        seed=_lightgbm_random_state(name_func("random_state")) if random_state is None else random_state,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/linear_model/_base.py b/hpsklearn/components/linear_model/_base.py
index a94c0a42..17634095 100644
--- a/hpsklearn/components/linear_model/_base.py
+++ b/hpsklearn/components/linear_model/_base.py
@@ -12,7 +12,8 @@ def linear_regression(name: str,
                       fit_intercept: bool = True,
                       copy_X: bool = True,
                       n_jobs: int = 1,
-                      positive: bool = False):
+                      positive: bool = False,
+                      **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.LinearRegression model.
@@ -32,7 +33,8 @@ def _name(msg):
         fit_intercept=fit_intercept,
         copy_X=copy_X,
         n_jobs=n_jobs,
-        positive=positive
+        positive=positive,
+        **kwargs
     )
 
     return scope.sklearn_LinearRegression(**hp_space)
diff --git a/hpsklearn/components/linear_model/_bayes.py b/hpsklearn/components/linear_model/_bayes.py
index 536daeb3..bdbfaabd 100644
--- a/hpsklearn/components/linear_model/_bayes.py
+++ b/hpsklearn/components/linear_model/_bayes.py
@@ -58,6 +58,7 @@ def _bayes_hp_space(
         fit_intercept: bool = True,
         copy_X: bool = True,
         verbose: bool = False,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -75,6 +76,7 @@ def _bayes_hp_space(
         fit_intercept=fit_intercept,
         copy_X=copy_X,
         verbose=verbose,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/linear_model/_coordinate_descent.py b/hpsklearn/components/linear_model/_coordinate_descent.py
index 319d11af..150fbbce 100644
--- a/hpsklearn/components/linear_model/_coordinate_descent.py
+++ b/hpsklearn/components/linear_model/_coordinate_descent.py
@@ -138,7 +138,8 @@ def _coordinate_descent_hp_space(
         tol: typing.Union[float, Apply] = None,
         warm_start: bool = False,
         random_state=None,
-        selection: typing.Union[str, Apply] = None
+        selection: typing.Union[str, Apply] = None,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -156,7 +157,8 @@ def _coordinate_descent_hp_space(
         warm_start=warm_start,
         random_state=_coordinate_descent_random_state(name_func("random_state"))
         if random_state is None else random_state,
-        selection=_coordinate_descent_selection(name_func("selection")) if selection is None else selection
+        selection=_coordinate_descent_selection(name_func("selection")) if selection is None else selection,
+        **kwargs
     )
     return hp_space
 
@@ -180,7 +182,8 @@ def _coordinate_descent_cv_hp_space(
         verbose: int = False,
         n_jobs: int = 1,
         random_state=None,
-        selection: typing.Union[str, Apply] = None
+        selection: typing.Union[str, Apply] = None,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -202,7 +205,8 @@ def _coordinate_descent_cv_hp_space(
         n_jobs=n_jobs,
         random_state=_coordinate_descent_random_state(name_func("random_state"))
         if random_state is None else random_state,
-        selection=_coordinate_descent_selection(name_func("selection")) if selection is None else selection
+        selection=_coordinate_descent_selection(name_func("selection")) if selection is None else selection,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/linear_model/_glm.py b/hpsklearn/components/linear_model/_glm.py
index 7accf1da..29ce6ed7 100644
--- a/hpsklearn/components/linear_model/_glm.py
+++ b/hpsklearn/components/linear_model/_glm.py
@@ -55,6 +55,7 @@ def _glm_hp_space(
         tol: typing.Union[float, Apply] = 1e-4,
         warm_start: bool = False,
         verbose: int = 0,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -68,7 +69,8 @@ def _glm_hp_space(
         max_iter=_glm_max_iter(name_func("max_iter")) if max_iter is None else max_iter,
         tol=_glm_tol(name_func("tol")) if tol is None else tol,
         warm_start=warm_start,
-        verbose=verbose
+        verbose=verbose,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/linear_model/_huber.py b/hpsklearn/components/linear_model/_huber.py
index aff16060..cc8fed75 100644
--- a/hpsklearn/components/linear_model/_huber.py
+++ b/hpsklearn/components/linear_model/_huber.py
@@ -50,7 +50,8 @@ def huber_regressor(name: str,
                     alpha: typing.Union[float, Apply] = None,
                     warm_start: bool = False,
                     fit_intercept: bool = True,
-                    tol: typing.Union[float, Apply] = None):
+                    tol: typing.Union[float, Apply] = None,
+                    **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.HuberRegressor model.
@@ -73,7 +74,8 @@ def _name(msg):
         alpha=_glm_alpha(_name("alpha")) if tol is None else alpha,
         warm_start=warm_start,
         fit_intercept=fit_intercept,
-        tol=_glm_tol(_name("tol")) if tol is None else tol
+        tol=_glm_tol(_name("tol")) if tol is None else tol,
+        **kwargs
     )
 
     return scope.sklearn_HuberRegressor(**hp_space)
diff --git a/hpsklearn/components/linear_model/_least_angle.py b/hpsklearn/components/linear_model/_least_angle.py
index 5fa14954..527aaf70 100644
--- a/hpsklearn/components/linear_model/_least_angle.py
+++ b/hpsklearn/components/linear_model/_least_angle.py
@@ -97,6 +97,7 @@ def _least_angle_hp_space(
         precompute: typing.Union[bool, str, npt.ArrayLike] = "auto",
         eps: float = np.finfo(float).eps,
         copy_X: bool = True,
+        **kwargs,
 ):
     """
     Hyper parameter of search space of common parameters for
@@ -111,7 +112,8 @@ def _least_angle_hp_space(
         verbose=verbose,
         precompute=precompute,
         eps=eps,
-        copy_X=copy_X
+        copy_X=copy_X,
+        **kwargs
     )
     return hp_space
 
@@ -126,7 +128,7 @@ def _least_angle_cv_shared_space(
         cv: typing.Union[int, callable, typing.Generator, Apply] = None,
         max_n_alphas: typing.Union[int, Apply] = None,
         n_jobs: int = None,
-):
+        **kwargs):
     """
     Declaration shared search space parameters for
      lars cv
@@ -148,8 +150,7 @@ def lars(name: str,
          fit_path: bool = True,
          jitter: float = None,
          random_state=None,
-         **kwargs
-         ):
+         **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.Lars model.
@@ -193,8 +194,7 @@ def lasso_lars(name: str,
                positive: bool = False,
                jitter: float = None,
                random_state=None,
-               **kwargs
-               ):
+               **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.LassoLars model.
@@ -235,8 +235,7 @@ def lars_cv(name: str,
             cv: typing.Union[int, callable, typing.Generator, Apply] = None,
             max_n_alphas: typing.Union[int, Apply] = None,
             n_jobs: int = None,
-            **kwargs
-            ):
+            **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.LarsCV model.
@@ -274,8 +273,7 @@ def lasso_lars_cv(name: str,
                   max_n_alphas: typing.Union[int, Apply] = None,
                   n_jobs: int = None,
                   positive: bool = False,
-                  **kwargs
-                  ):
+                  **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.LassoLarsCV model.
@@ -315,8 +313,7 @@ def lasso_lars_ic(name: str,
                   criterion: typing.Union[str, Apply] = None,
                   max_iter: typing.Union[int, Apply] = None,
                   positive: bool = False,
-                  **kwargs
-                  ):
+                  **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.LassoLarsIC model.
diff --git a/hpsklearn/components/linear_model/_logistic.py b/hpsklearn/components/linear_model/_logistic.py
index f3481b20..a351a919 100644
--- a/hpsklearn/components/linear_model/_logistic.py
+++ b/hpsklearn/components/linear_model/_logistic.py
@@ -89,9 +89,6 @@ def _logistic_random_state(name: str):
                                                                                 "sag", "saga"],
           msg="Invalid parameter '%s' with value '%s'. "
               "Value must be in ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'].")
-@validate(params=["multi_class"],
-          validation_test=lambda param: not isinstance(param, str) or param in ["auto", "ovr", "multinomial"],
-          msg="Invalid parameter '%s' with value '%s'. Value must be in ['auto', 'ovr', 'multinomial'].")
 def _logistic_hp_space(
         name_func,
         fit_intercept: bool = True,
@@ -104,8 +101,8 @@ def _logistic_hp_space(
         n_jobs: int = 1,
         verbose: int = 0,
         intercept_scaling: float = 1,
-        multi_class: str = "auto",
-        random_state=None
+        random_state=None,
+        **kwargs
 ):
     if dual is True and not (penalty == "l2" and solver == "liblinear"):
         raise ValueError("Dual formulation (implied by parameter 'dual' = 'True') is only implemented for "
@@ -125,9 +122,9 @@ def _logistic_hp_space(
         n_jobs=n_jobs,
         verbose=verbose,
         intercept_scaling=intercept_scaling,
-        multi_class=multi_class,
         random_state=_logistic_random_state(name_func("random_state"))
         if random_state is None else random_state,
+        **kwargs,
     )
     return hp_space
 
diff --git a/hpsklearn/components/linear_model/_omp.py b/hpsklearn/components/linear_model/_omp.py
index 5ea2e28c..bccfceae 100644
--- a/hpsklearn/components/linear_model/_omp.py
+++ b/hpsklearn/components/linear_model/_omp.py
@@ -20,8 +20,8 @@ def orthogonal_matching_pursuit(name: str,
                                 n_nonzero_coefs: int = None,
                                 tol: typing.Union[float, Apply] = None,
                                 fit_intercept: bool = True,
-                                precompute: typing.Union[str, bool] = "auto"
-                                ):
+                                precompute: typing.Union[str, bool] = "auto",
+                                **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.OrthogonalMatchingPursuit model.
@@ -41,7 +41,8 @@ def _name(msg):
         n_nonzero_coefs=n_nonzero_coefs,
         tol=hp.loguniform(_name("tol"), np.log(1e-5), np.log(1e-2)) if tol is None else tol,
         fit_intercept=fit_intercept,
-        precompute=precompute
+        precompute=precompute,
+        **kwargs
     )
 
     return scope.sklearn_OrthogonalMatchingPursuit(**hp_space)
@@ -53,8 +54,8 @@ def orthogonal_matching_pursuit_cv(name: str,
                                    max_iter: typing.Union[int, Apply] = None,
                                    cv: typing.Union[int, callable, typing.Generator, Apply] = None,
                                    n_jobs: int = 1,
-                                   verbose: typing.Union[bool, int] = False
-                                   ):
+                                   verbose: typing.Union[bool, int] = False,
+                                   **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.OrthogonalMatchingPursuitCV model.
@@ -79,7 +80,8 @@ def _name(msg):
         cv=hp.pchoice(_name("cv"), [(0.0625, 3), (0.175, 4), (0.525, 5), (0.175, 6), (0.0625, 7)])
         if cv is None else cv,
         n_jobs=n_jobs,
-        verbose=verbose
+        verbose=verbose,
+        **kwargs
     )
 
     return scope.sklearn_OrthogonalMatchingPursuitCV(**hp_space)
diff --git a/hpsklearn/components/linear_model/_passive_aggressive.py b/hpsklearn/components/linear_model/_passive_aggressive.py
index f8f14a85..38b0fe33 100644
--- a/hpsklearn/components/linear_model/_passive_aggressive.py
+++ b/hpsklearn/components/linear_model/_passive_aggressive.py
@@ -68,7 +68,8 @@ def _passive_aggressive_hp_space(
         verbose: int = 0,
         random_state=None,
         warm_start: bool = False,
-        average: typing.Union[bool, int] = False
+        average: typing.Union[bool, int] = False,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -89,7 +90,8 @@ def _passive_aggressive_hp_space(
         random_state=_passive_aggressive_random_state(name_func("random_state"))
         if random_state is None else random_state,
         warm_start=warm_start,
-        average=average
+        average=average,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/linear_model/_perceptron.py b/hpsklearn/components/linear_model/_perceptron.py
index fe94ed6e..b311efcf 100644
--- a/hpsklearn/components/linear_model/_perceptron.py
+++ b/hpsklearn/components/linear_model/_perceptron.py
@@ -32,8 +32,8 @@ def perceptron(name: str,
                validation_fraction: typing.Union[float, Apply] = None,
                n_iter_no_change: typing.Union[int, Apply] = 5,
                class_weight: typing.Union[dict, str] = None,
-               warm_start: bool = False
-               ):
+               warm_start: bool = False,
+               **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.Perceptron model.
@@ -78,7 +78,8 @@ def _name(msg):
         n_iter_no_change=hp.pchoice(_name("n_iter_no_change"), [(0.25, 4), (0.50, 5), (0.25, 6)])
         if n_iter_no_change is None else n_iter_no_change,
         class_weight=class_weight,
-        warm_start=warm_start
+        warm_start=warm_start,
+        **kwargs
     )
 
     return scope.sklearn_Perceptron(**hp_space)
diff --git a/hpsklearn/components/linear_model/_quantile.py b/hpsklearn/components/linear_model/_quantile.py
index 00326ed5..7bd26809 100644
--- a/hpsklearn/components/linear_model/_quantile.py
+++ b/hpsklearn/components/linear_model/_quantile.py
@@ -22,7 +22,8 @@ def quantile_regression(name: str,
                         alpha: typing.Union[float, Apply] = None,
                         fit_intercept: typing.Union[bool, Apply] = None,
                         solver: typing.Union[str, Apply] = None,
-                        solver_options: dict = None):
+                        solver_options: dict = None,
+                        **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.QuantileRegression model.
@@ -45,7 +46,8 @@ def _name(msg):
         fit_intercept=hp.choice(_name("fit_intercept"), [True, False]) if fit_intercept is None else fit_intercept,
         solver=hp.choice(_name("solver"), ["highs-ds", "highs-ipm", "highs", "revised simplex"])
         if solver is None else solver,
-        solver_options=solver_options
+        solver_options=solver_options,
+        **kwargs
     )
 
     return scope.sklearn_QuantileRegressor(**hp_space)
diff --git a/hpsklearn/components/linear_model/_ransac.py b/hpsklearn/components/linear_model/_ransac.py
index 399726c8..53a63c72 100644
--- a/hpsklearn/components/linear_model/_ransac.py
+++ b/hpsklearn/components/linear_model/_ransac.py
@@ -28,7 +28,8 @@ def ransac_regression(name: str,
                       stop_score: typing.Union[float, Apply] = None,
                       stop_probability: typing.Union[float, Apply] = None,
                       loss: typing.Union[callable, str, Apply] = None,
-                      random_state=None):
+                      random_state=None,
+                      **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.RANSACRegressor model.
@@ -65,7 +66,8 @@ def _name(msg):
         stop_probability=hp.uniform(_name("stop_probability"), 0.90, 0.99)
         if stop_probability is None else stop_probability,
         loss=hp.choice(_name("loss"), ["absolute_error", "squared_error"]) if loss is None else loss,
-        random_state=hp.randint(_name("random_state"), 5) if random_state is None else random_state
+        random_state=hp.randint(_name("random_state"), 5) if random_state is None else random_state,
+        **kwargs
     )
 
     return scope.sklearn_RANSACRegressor(**hp_space)
diff --git a/hpsklearn/components/linear_model/_ridge.py b/hpsklearn/components/linear_model/_ridge.py
index 4493f5e9..570700cf 100644
--- a/hpsklearn/components/linear_model/_ridge.py
+++ b/hpsklearn/components/linear_model/_ridge.py
@@ -94,6 +94,7 @@ def _ridge_hp_space(
         solver: typing.Union[str, Apply] = "auto",
         positive: bool = False,
         random_state=None,
+        **kwargs,
 ):
     """
     Hyper parameter search space for
@@ -108,7 +109,8 @@ def _ridge_hp_space(
         tol=_ridge_tol(name_func("tol")) if tol is None else tol,
         solver=solver,
         positive=positive,
-        random_state=random_state
+        random_state=random_state,
+        **kwargs
     )
     return hp_space
 
@@ -119,7 +121,7 @@ def _ridge_cv_hp_space(
         fit_intercept: bool = True,
         scoring: typing.Union[str, callable] = None,
         cv: typing.Union[int, Iterable, typing.Generator, Apply] = None,
-        store_cv_values: bool = False
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -131,7 +133,7 @@ def _ridge_cv_hp_space(
         fit_intercept=fit_intercept,
         scoring=scoring,
         cv=_ridge_cv(name_func("cv")) if cv is None else cv,
-        store_cv_values=store_cv_values
+        **kwargs
     )
     return hp_space
 
@@ -162,6 +164,7 @@ def _name(msg):
 def ridge_cv(name: str,
              gcv_mode: typing.Union[str, Apply] = "auto",
              alpha_per_target: bool = False,
+             store_cv_results: bool = False,
              **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
@@ -182,6 +185,7 @@ def _name(msg):
     hp_space = _ridge_cv_hp_space(_name, **kwargs)
     hp_space["gcv_mode"] = gcv_mode
     hp_space["alpha_per_target"] = alpha_per_target
+    hp_space["store_cv_results"] = store_cv_results
 
     return scope.sklearn_RidgeCV(**hp_space)
 
@@ -214,7 +218,10 @@ def _name(msg):
 @validate(params=["class_weight"],
           validation_test=lambda param: not isinstance(param, str) or param == "balanced",
           msg="Invalid parameter '%s' with value '%s'. Value must be 'balanced'")
-def ridge_classifier_cv(name: str, class_weight: typing.Union[dict, str] = None, **kwargs):
+def ridge_classifier_cv(name: str,
+                        class_weight: typing.Union[dict, str] = None,
+                        store_cv_results: bool = False,
+                        **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.RidgeClassifierCV model.
@@ -232,5 +239,6 @@ def _name(msg):
 
     hp_space = _ridge_cv_hp_space(_name, **kwargs)
     hp_space["class_weight"] = class_weight
+    hp_space["store_cv_results"] = store_cv_results
 
     return scope.sklearn_RidgeClassifierCV(**hp_space)
diff --git a/hpsklearn/components/linear_model/_stochastic_gradient.py b/hpsklearn/components/linear_model/_stochastic_gradient.py
index 7f73141d..5c3f8fbb 100644
--- a/hpsklearn/components/linear_model/_stochastic_gradient.py
+++ b/hpsklearn/components/linear_model/_stochastic_gradient.py
@@ -176,6 +176,7 @@ def _stochastic_gradient_hp_space(
         n_iter_no_change: typing.Union[int, Apply] = None,
         warm_start: bool = False,
         average: typing.Union[bool, int] = False,
+        **kwargs,
 ):
     """
     Hyper parameter search space for
@@ -212,6 +213,7 @@ def _stochastic_gradient_hp_space(
         if n_iter_no_change is None else n_iter_no_change,
         warm_start=warm_start,
         average=average,
+        **kwargs,
     )
     return hp_space
 
@@ -297,8 +299,8 @@ def sgd_one_class_svm(name: str,
                       eta0: typing.Union[float, Apply] = None,
                       power_t: typing.Union[float, Apply] = None,
                       warm_start: bool = False,
-                      average: typing.Union[bool, int] = False
-                      ):
+                      average: typing.Union[bool, int] = False,
+                      **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.SGDOneClassSVM model.
@@ -339,6 +341,7 @@ def _name(msg):
         eta0=_stochastic_gradient_eta0(_name("eta0")) if eta0 is None else eta0,
         power_t=_stochastic_gradient_power_t(_name("power_t")) if power_t is None else power_t,
         warm_start=warm_start,
-        average=average
+        average=average,
+        **kwargs
     )
     return scope.sklearn_SGDOneClassSVM(**hp_space)
diff --git a/hpsklearn/components/linear_model/_theil_sen.py b/hpsklearn/components/linear_model/_theil_sen.py
index 2a0f0f19..bd8b8f1a 100644
--- a/hpsklearn/components/linear_model/_theil_sen.py
+++ b/hpsklearn/components/linear_model/_theil_sen.py
@@ -19,14 +19,14 @@ def sklearn_TheilSenRegressor(*args, **kwargs):
 def theil_sen_regressor(
         name: str,
         fit_intercept: bool = True,
-        copy_X: bool = True,
         max_subpopulation: typing.Union[int, Apply] = None,
         n_subsamples: int = None,
         max_iter: typing.Union[int, Apply] = None,
         tol: typing.Union[float, Apply] = None,
         random_state=None,
         n_jobs: int = 1,
-        verbose: bool = False):
+        verbose: bool = False,
+        **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.linear_model.TheilSenRegressor model.
@@ -34,7 +34,6 @@ def theil_sen_regressor(
     Args:
         name: name | str
         fit_intercept: whether to calculate the intercept | bool
-        copy_X: whether to copy X | bool
         max_subpopulation: consider stochastic subpopulation | int
         n_subsamples: number of samples to calculate parameters | int
         max_iter: maximum number of iterations | int
@@ -49,7 +48,6 @@ def _name(msg):
 
     hp_space = dict(
         fit_intercept=fit_intercept,
-        copy_X=copy_X,
         max_subpopulation=scope.int(hp.uniform(_name("max_subpopulation"), 7500, 12500))
         if max_subpopulation is None else max_subpopulation,
         n_subsamples=n_subsamples,
@@ -57,6 +55,7 @@ def _name(msg):
         tol=hp.loguniform(_name("tol"), np.log(1e-5), np.log(1e-2)) if tol is None else tol,
         random_state=hp.randint(_name("random_state"), 5) if random_state is None else random_state,
         n_jobs=n_jobs,
-        verbose=verbose
+        verbose=verbose,
+        **kwargs
     )
     return scope.sklearn_TheilSenRegressor(**hp_space)
diff --git a/hpsklearn/components/mixture/_bayesian_mixture.py b/hpsklearn/components/mixture/_bayesian_mixture.py
index d8a123b4..d0b0d0aa 100644
--- a/hpsklearn/components/mixture/_bayesian_mixture.py
+++ b/hpsklearn/components/mixture/_bayesian_mixture.py
@@ -41,7 +41,8 @@ def bayesian_gaussian_mixture(name: str,
                               random_state=None,
                               warm_start: bool = False,
                               verbose: int = 0,
-                              verbose_interval: int = 10):
+                              verbose_interval: int = 10,
+                              **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.mixture.BayesianGaussianMixture model.
@@ -91,5 +92,6 @@ def _name(msg):
         warm_start=warm_start,
         verbose=verbose,
         verbose_interval=verbose_interval,
+        **kwargs,
     )
     return scope.sklearn_BayesianGaussianMixture(**hp_space)
diff --git a/hpsklearn/components/mixture/_gaussian_mixture.py b/hpsklearn/components/mixture/_gaussian_mixture.py
index e5a30d8a..e83d7648 100644
--- a/hpsklearn/components/mixture/_gaussian_mixture.py
+++ b/hpsklearn/components/mixture/_gaussian_mixture.py
@@ -34,7 +34,8 @@ def gaussian_mixture(name: str,
                      random_state=None,
                      warm_start: bool = False,
                      verbose: int = 0,
-                     verbose_interval: int = 10):
+                     verbose_interval: int = 10,
+                     **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.mixture.GaussianMixture model.
@@ -76,5 +77,6 @@ def _name(msg):
         warm_start=warm_start,
         verbose=verbose,
         verbose_interval=verbose_interval,
+        **kwargs,
     )
     return scope.sklearn_GaussianMixture(**hp_space)
diff --git a/hpsklearn/components/multiclass.py b/hpsklearn/components/multiclass.py
index 01dc821c..10d3e8a8 100644
--- a/hpsklearn/components/multiclass.py
+++ b/hpsklearn/components/multiclass.py
@@ -23,7 +23,8 @@ def sklearn_OutputCodeClassifier(*args, **kwargs):
 
 def one_vs_rest_classifier(name: str,
                            estimator: typing.Union[object, Apply] = None,
-                           n_jobs: int = 1):
+                           n_jobs: int = 1,
+                           **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.multiclass.OneVsRestClassifier model.
@@ -39,14 +40,16 @@ def _name(msg):
 
     hp_space = dict(
         estimator=any_classifier(_name("estimator")) if estimator is None else estimator,
-        n_jobs=n_jobs
+        n_jobs=n_jobs,
+        **kwargs
     )
     return scope.sklearn_OneVsRestClassifier(**hp_space)
 
 
 def one_vs_one_classifier(name: str,
                           estimator: typing.Union[object, Apply] = None,
-                          n_jobs: int = 1):
+                          n_jobs: int = 1,
+                          **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.multiclass.OneVsOneClassifier model.
@@ -62,7 +65,8 @@ def _name(msg):
 
     hp_space = dict(
         estimator=any_classifier(_name("estimator")) if estimator is None else estimator,
-        n_jobs=n_jobs
+        n_jobs=n_jobs,
+        **kwargs
     )
     return scope.sklearn_OneVsOneClassifier(**hp_space)
 
@@ -71,7 +75,8 @@ def output_code_classifier(name: str,
                            estimator: typing.Union[object, Apply] = None,
                            code_size: typing.Union[float, Apply] = None,
                            random_state=None,
-                           n_jobs: int = 1):
+                           n_jobs: int = 1,
+                           **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.multiclass.OutputCodeClassifier model.
@@ -91,6 +96,7 @@ def _name(msg):
         estimator=any_classifier(_name("estimator")) if estimator is None else estimator,
         code_size=hp.uniform(_name("code_size"), 1, 2) if code_size is None else code_size,
         n_jobs=n_jobs,
-        random_state=hp.randint(_name("random_state"), 5) if random_state is None else random_state
+        random_state=hp.randint(_name("random_state"), 5) if random_state is None else random_state,
+        **kwargs
     )
     return scope.sklearn_OutputCodeClassifier(**hp_space)
diff --git a/hpsklearn/components/naive_bayes.py b/hpsklearn/components/naive_bayes.py
index 180cd2f1..b8a6380c 100644
--- a/hpsklearn/components/naive_bayes.py
+++ b/hpsklearn/components/naive_bayes.py
@@ -37,6 +37,7 @@ def _nb_hp_space(
         alpha: typing.Union[float, Apply] = None,
         fit_prior: typing.Union[bool, Apply] = None,
         class_prior: npt.ArrayLike = None,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -48,7 +49,8 @@ def _nb_hp_space(
     hp_space = dict(
         alpha=hp.quniform(name_func("alpha"), 0, 1, 0.001) if alpha is None else alpha,
         fit_prior=hp.choice(name_func("fit_prior"), [True, False]) if fit_prior is None else fit_prior,
-        class_prior=class_prior
+        class_prior=class_prior,
+        **kwargs
     )
     return hp_space
 
@@ -119,7 +121,7 @@ def _name(msg):
     return scope.sklearn_ComplementNB(**hp_space)
 
 
-def gaussian_nb(name: str, var_smoothing: float = 1e-9):
+def gaussian_nb(name: str, var_smoothing: float = 1e-9, **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.naive_bayes.GaussianNB model.
@@ -133,7 +135,8 @@ def _name(msg):
         return f"{name}.gaussian_nb_{msg}"
 
     hp_space = dict(
-        var_smoothing=var_smoothing
+        var_smoothing=var_smoothing,
+        **kwargs
     )
     return scope.sklearn_GaussianNB(**hp_space)
 
diff --git a/hpsklearn/components/neighbors/_nearest_centroid.py b/hpsklearn/components/neighbors/_nearest_centroid.py
index 7e84f8e5..e83e49a6 100644
--- a/hpsklearn/components/neighbors/_nearest_centroid.py
+++ b/hpsklearn/components/neighbors/_nearest_centroid.py
@@ -12,7 +12,8 @@ def sklearn_NearestCentroid(*args, **kwargs):
 
 def nearest_centroid(name: str,
                      metric: typing.Union[str, Apply] = None,
-                     shrink_threshold: float = None):
+                     shrink_threshold: float = None,
+                     **kwargs):
     """
     Return a pyll graph with hyperparameters that will construct
     a sklearn.neighbors.NearestCentroid model.
@@ -30,5 +31,6 @@ def _name(msg):
         metric=hp.choice(_name("metric"), ["euclidean", "manhattan"])
         if metric is None else metric,
         shrink_threshold=shrink_threshold,
+        **kwargs,
     )
     return scope.sklearn_NearestCentroid(**hp_space)
diff --git a/hpsklearn/components/neighbors/_regression.py b/hpsklearn/components/neighbors/_regression.py
index 5cee1239..303dfae9 100644
--- a/hpsklearn/components/neighbors/_regression.py
+++ b/hpsklearn/components/neighbors/_regression.py
@@ -67,7 +67,8 @@ def neighbors_hp_space(
         p: typing.Union[int, Apply] = None,
         metric: typing.Union[str, callable, Apply] = None,
         metric_params: dict = None,
-        n_jobs: int = 1):
+        n_jobs: int = 1,
+        **kwargs):
     """
     Hyper parameter search space for
      k neighbors regressor
@@ -80,7 +81,8 @@ def neighbors_hp_space(
         p=_neighbors_p(name_func("p")) if p is None else p,
         metric=_neighbors_metric(name_func("metric")) if metric is None else metric,
         metric_params=metric_params,
-        n_jobs=n_jobs
+        n_jobs=n_jobs,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/neural_network/_multilayer_perceptron.py b/hpsklearn/components/neural_network/_multilayer_perceptron.py
index 41523505..d9e38bcf 100644
--- a/hpsklearn/components/neural_network/_multilayer_perceptron.py
+++ b/hpsklearn/components/neural_network/_multilayer_perceptron.py
@@ -177,6 +177,7 @@ def _multilayer_perceptron_hp_space(
         epsilon: typing.Union[float, Apply] = None,
         n_iter_no_change: typing.Union[int, Apply] = None,
         max_fun: typing.Union[int, Apply] = None,
+        **kwargs,
 ):
     """
     Hyper parameter search space for
@@ -214,6 +215,7 @@ def _multilayer_perceptron_hp_space(
         n_iter_no_change=_multilayer_perceptron_n_iter_no_change(name_func("n_iter_no_change"))
         if n_iter_no_change is None else n_iter_no_change,
         max_fun=_multilayer_perceptron_max_fun(name_func("max_fun")) if max_fun is None else max_fun,
+        **kwargs,
     )
     return hp_space
 
diff --git a/hpsklearn/components/semi_supervised/_label_propagation.py b/hpsklearn/components/semi_supervised/_label_propagation.py
index 83b75e23..17f6c298 100644
--- a/hpsklearn/components/semi_supervised/_label_propagation.py
+++ b/hpsklearn/components/semi_supervised/_label_propagation.py
@@ -55,7 +55,8 @@ def _label_propagation_hp_space(
         gamma: typing.Union[float, Apply] = None,
         n_neighbors: typing.Union[int, Apply] = None,
         tol: typing.Union[float, Apply] = None,
-        n_jobs: int = 1
+        n_jobs: int = 1,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -67,7 +68,8 @@ def _label_propagation_hp_space(
         gamma=_label_propagation_gamma(name_func("gamma")) if gamma is None else gamma,
         n_neighbors=_label_propagation_n_neighbors(name_func("n_neighbors")) if n_neighbors is None else n_neighbors,
         tol=_label_propagation_n_tol(name_func("tol")) if tol is None else tol,
-        n_jobs=n_jobs
+        n_jobs=n_jobs,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/svm/_classes.py b/hpsklearn/components/svm/_classes.py
index 06c2d411..8557c63c 100644
--- a/hpsklearn/components/svm/_classes.py
+++ b/hpsklearn/components/svm/_classes.py
@@ -133,10 +133,11 @@ def _linear_hp_space(
         C: typing.Union[float, Apply] = None,
         fit_intercept: bool = True,
         intercept_scaling: typing.Union[float, Apply] = None,
-        dual: bool = True,
+        dual: bool = "auto",
         verbose: int = 0,
         random_state=None,
-        max_iter: typing.Union[int, Apply] = None
+        max_iter: typing.Union[int, Apply] = None,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -152,7 +153,8 @@ def _linear_hp_space(
         dual=dual,
         verbose=verbose,
         random_state=_svm_random_state(name_func("random_state")) if random_state is None else random_state,
-        max_iter=_linear_max_iter(name_func("max_iter")) if max_iter is None else max_iter
+        max_iter=_linear_max_iter(name_func("max_iter")) if max_iter is None else max_iter,
+        **kwargs
     )
     return hp_space
 
@@ -185,7 +187,8 @@ def _svc_hp_space(name_func,
                   max_iter: typing.Union[int, Apply] = None,
                   decision_function_shape: typing.Union[str, Apply] = None,
                   break_ties: bool = False,
-                  random_state=None):
+                  random_state=None,
+                  **kwargs):
     """
     Hyper parameter search space for
      nu svc
@@ -206,7 +209,8 @@ def _svc_hp_space(name_func,
         decision_function_shape=_svc_decision_function_shape(name_func("decision_function_shape"))
         if decision_function_shape is None else decision_function_shape,
         break_ties=break_ties,
-        random_state=_svm_random_state(name_func("random_state")) if random_state is None else random_state
+        random_state=_svm_random_state(name_func("random_state")) if random_state is None else random_state,
+        **kwargs
     )
     return hp_space
 
@@ -220,7 +224,8 @@ def _svr_one_class_hp_space(name_func,
                             shrinking: typing.Union[bool, Apply] = None,
                             cache_size: int = 200,
                             verbose: bool = False,
-                            max_iter: int = None):
+                            max_iter: int = None,
+                            **kwargs):
     """
     Hyper parameter search space for
      nu svr
@@ -237,6 +242,7 @@ def _svr_one_class_hp_space(name_func,
         cache_size=cache_size,
         verbose=verbose,
         max_iter=-1 if max_iter is None else max_iter,
+        **kwargs,
     )
     return hp_space
 
diff --git a/hpsklearn/components/tree/_classes.py b/hpsklearn/components/tree/_classes.py
index d6788e37..d70c7b09 100644
--- a/hpsklearn/components/tree/_classes.py
+++ b/hpsklearn/components/tree/_classes.py
@@ -144,7 +144,8 @@ def _tree_hp_space(
         random_state=None,
         min_impurity_decrease: typing.Union[float, Apply] = 0.0,
         max_leaf_nodes: typing.Union[int, Apply] = "Undefined",
-        ccp_alpha: float = 0.0
+        ccp_alpha: float = 0.0,
+        **kwargs
 ):
     """
     Hyper parameter search space for
@@ -168,7 +169,8 @@ def _tree_hp_space(
         if min_impurity_decrease is None else min_impurity_decrease,
         max_leaf_nodes=_tree_max_leaf_nodes(name_func("max_leaf_nodes"))
         if max_leaf_nodes == "Undefined" else max_leaf_nodes,
-        ccp_alpha=ccp_alpha
+        ccp_alpha=ccp_alpha,
+        **kwargs
     )
     return hp_space
 
diff --git a/hpsklearn/components/xgboost.py b/hpsklearn/components/xgboost.py
index f4120ad0..6c6b8522 100644
--- a/hpsklearn/components/xgboost.py
+++ b/hpsklearn/components/xgboost.py
@@ -119,7 +119,8 @@ def _xgboost_hp_space(
         scale_pos_weight: float = 1,
         base_score: float = 0.5,
         random_state=None,
-        n_jobs: int = -1):
+        n_jobs: int = -1,
+        **kwargs):
     """
     Hyper parameter search space for
      xgboost classifier
@@ -143,7 +144,8 @@ def _xgboost_hp_space(
         scale_pos_weight=scale_pos_weight,
         base_score=base_score,
         seed=_xgboost_random_state(name_func("random_state")) if random_state is None else random_state,
-        n_jobs=n_jobs
+        n_jobs=n_jobs,
+        **kwargs
     )
     return hp_space
 
@@ -169,7 +171,6 @@ def _name(msg):
 
     hp_space = _xgboost_hp_space(_name, **kwargs)
     hp_space["objective"] = "binary:logistic" if objective is None else objective
-    hp_space["use_label_encoder"] = False
 
     return scope.sklearn_XGBClassifier(**hp_space)
 
diff --git a/pyproject.toml b/pyproject.toml
index 64ef545a..86c57f55 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,3 +1,3 @@
 [build-system]
-requires = ["setuptools>=68.2.2", "wheel"]
+requires = ["setuptools>=71.0.0", "wheel"]
 build-backend = "setuptools.build_meta"
diff --git a/requirements.txt b/requirements.txt
index bafd0a5e..39ebfbb0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,6 @@
-hyperopt>=0.2.6,<=0.2.7
-numpy>=1.26.0,<1.26.1
-scikit-learn>=1.3.0,<=1.3.2
-scipy>=1.11.2,<=1.11.3
-pandas>=2.1.0,<=2.1.1
+hyperopt==0.2.7
+numpy>=2.0.0,<=2.3.0
+scikit-learn>=1.5,<=1.7
+scipy>=1.15.0,<=1.15.3
+pandas>=2.1.0,<=2.3.0
+setuptools>=71.0.0
\ No newline at end of file
diff --git a/requirements_dev.txt b/requirements_dev.txt
index b21c8dbf..2b72fa20 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -1,5 +1,5 @@
-tox==4.8.0,<=4.11.3
-xgboost==1.6.2
-lightgbm>=4.0.0,<4.1.0
-coverage==7.3.1
+tox>=4.20.0
+xgboost>=2.0.0,<=2.2.0
+lightgbm==4.6.0
+coverage==7.6.12
 -r requirements.txt
diff --git a/setup.cfg b/setup.cfg
index 588952dc..be0e8c13 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,17 +1,19 @@
 [metadata]
-name = hpsklearn
-version = 1.0.3
+name = hyperopt-sklearn
+version = 1.1.0
 description = Hyperparameter Optimization for sklearn
 long_description = file: README.md
-url = http://hyperopt.github.com/hyperopt-sklearn/
+url = https://github.com/hyperopt/hyperopt-sklearn/
 author = James Bergstra
 author_email = anon@anon.com
+maintainer = Pim Tholhuijsen
+maintainer_email = anon@anon.com
 license = BSD
-license_file = LICENSE.txt
+license_files = LICENSE.txt
 platforms = Linux, OS-X, Windows
 keywords = hyperopt, hyperparameter, sklearn
 classifiers =
-    Development Status :: 1.0.3 - Alpha
+    Development Status :: 3 - Alpha
     Intended Audience :: Education
     Intended Audience :: Science/Research
     Intended Audience :: Developers
@@ -23,25 +25,27 @@ classifiers =
     Operating System :: Unix
     Programming Language :: Python :: 3
     Programming Language :: Python :: 3 :: Only
-    Programming Language :: Python :: 3.9
-    Programming Language :: Python :: 3.10
     Programming Language :: Python :: 3.11
+    Programming Language :: Python :: 3.12
+    Programming Language :: Python :: 3.13
     Topic :: Scientific/Engineering
     Topic :: Software Development
 
 [options]
-packages =
-    find:
+packages = find:
 install_requires =
-    hyperopt>=0.2.6
-    numpy>=1.26.0
-    scikit-learn>=1.3.0
-    scipy>=1.11.2
-python_requires = >=3.9
+    hyperopt==0.2.7
+    numpy>=2.0.0,<=2.3.0
+    scikit-learn>=1.5,<=1.7
+    scipy>=1.15.0,<=1.15.3
+    pandas>=2.1.0,<=2.3.0
+    setuptools>=71.0.0
+python_requires = >=3.11
 zip_safe = False
 
 [options.extras_require]
-xgboost = xgboost>=2.0.0
-lightgbm = lightgbm>=4.0.0
+xgboost = xgboost>=2.0.0,<=2.2.0
+lightgbm = lightgbm==4.6.0
 testing =
-    tox>=4.11.3
+    tox>=4.20.0
+    coverage==7.6.12
\ No newline at end of file
diff --git a/tests/test_components/test_feature_extraction/test_text.py b/tests/test_components/test_feature_extraction/test_text.py
index c3cdacd1..6b974b90 100644
--- a/tests/test_components/test_feature_extraction/test_text.py
+++ b/tests/test_components/test_feature_extraction/test_text.py
@@ -2,8 +2,11 @@
 
 from hpsklearn import \
     HyperoptEstimator, \
-    tfidf, \
-    multinomial_nb
+    multinomial_nb, \
+    sgd_classifier, \
+    tfidf_vectorizer, \
+    hashing_vectorizer, \
+    count_vectorizer
 from tests.utils import TrialsExceptionHandler
 from hyperopt import rand
 
@@ -36,7 +39,7 @@ def test_tfidf_vectorizer(self):
         """
         model = HyperoptEstimator(
             classifier=multinomial_nb("classifier"),
-            preprocessing=[tfidf("preprocessing")],
+            preprocessing=[tfidf_vectorizer("preprocessing")],
             algo=rand.suggest,
             trial_timeout=10.0,
             max_evals=5,
@@ -44,7 +47,41 @@ def test_tfidf_vectorizer(self):
         model.fit(self.X_test, self.Y_test)
         model.score(self.X_test, self.Y_test)
 
-    test_tfidf_vectorizer.__name__ = f"test_{tfidf.__name__}"
+    @TrialsExceptionHandler
+    def test_hashing_vectorizer(self):
+        """
+        Instantiate sgd_classifier hyperopt_estimator model
+         add HashingVectorizer preprocessor
+         fit and score model
+        """
+        model = HyperoptEstimator(
+            classifier=sgd_classifier("classifier"),
+            preprocessing=[hashing_vectorizer("preprocessing")],
+            algo=rand.suggest,
+            trial_timeout=10.0,
+            max_evals=5,
+        )
+        model.fit(self.X_test, self.Y_test)
+
+    @TrialsExceptionHandler
+    def test_count_vectorizer(self):
+        """
+        Instantiate multinomial_nb hyperopt_estimator model
+         add CountVectorizer preprocessor
+         fit and score model
+        """
+        model = HyperoptEstimator(
+            classifier=multinomial_nb("classifier"),
+            preprocessing=[count_vectorizer("preprocessing")],
+            algo=rand.suggest,
+            trial_timeout=10.0,
+            max_evals=5,
+        )
+        model.fit(self.X_test, self.Y_test)
+
+    test_tfidf_vectorizer.__name__ = f"test_{tfidf_vectorizer.__name__}"
+    test_hashing_vectorizer.__name__ = f"test_{hashing_vectorizer.__name__}"
+    test_count_vectorizer.__name__ = f"test_{count_vectorizer.__name__}"
 
 
 if __name__ == "__main__":
diff --git a/tests/test_estimator/test_estimator.py b/tests/test_estimator/test_estimator.py
index 95864647..d0587fd0 100644
--- a/tests/test_estimator/test_estimator.py
+++ b/tests/test_estimator/test_estimator.py
@@ -5,7 +5,7 @@
 from hyperopt.pyll import as_apply
 from hpsklearn import HyperoptEstimator
 from hpsklearn import sgd_classifier, any_classifier
-from ..utils import RetryOnTrialsException
+from tests.utils import RetryOnTrialsException
 
 
 class TestIter(unittest.TestCase):
diff --git a/tox.ini b/tox.ini
index 1ba1ea4f..7127bb32 100644
--- a/tox.ini
+++ b/tox.ini
@@ -1,13 +1,13 @@
 [tox]
 minversion = 3.7.0
-envlist = py39, py310, py311, flake8
+envlist = py311, py312, py313, flake8
 isolated_build = true
 
 [gh-actions]
 python =
-    3.9: py39
-    3.10: py310
-    3.11: py311, flake8
+    3.11: py311
+    3.12: py312
+    3.13: py313, flake8
 
 [testenv]
 setenv =
@@ -19,6 +19,6 @@ commands =
     coverage report -m
 
 [testenv:flake8]
-basepython = python3.11
+basepython = python3.13
 deps = flake8
 commands = flake8 --max-line-length=120 --ignore=F401,W504 hpsklearn tests