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TESTa53a1db8f6sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
1 runs0 likes0 downloads0 reach0 impact
TESTfd5f45aa24sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TEST5ba06d4b1esklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TEST2d9e310947sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
1 runs0 likes0 downloads0 reach0 impact
TEST5d727cca9esklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
1 runs0 likes0 downloads0 reach0 impact
TEST159178f4c1sklearn.linear_model._logistic.LogisticRegression (1)
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
1 runs0 likes0 downloads0 reach0 impact
TESTe73c2adf37sklearn.linear_model._base.LinearRegression (1)
Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset,…
1 runs0 likes0 downloads0 reach0 impact
TESTcc6f557e19sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST3ce618a4f1sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…
1 runs0 likes0 downloads0 reach0 impact
TEST75b6f208c8sklearn.linear_model._logistic.LogisticRegression (1)
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
1 runs0 likes0 downloads0 reach0 impact
TEST0a8afea781sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TESTTEST1152974d1csklearn.naive_bayes.GaussianNB (1)
Gaussian Naive Bayes (GaussianNB). Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…
1 runs0 likes0 downloads0 reach0 impact
TESTab31dc0305sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TEST2bc0c18170sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.svm._classes.SVC)) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TESTb780ade6f5sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.svm._classes.SVC)) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "predict",…
1 runs0 likes0 downloads0 reach0 impact
TEST0c1d556796sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TESTae82ed4f7dsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TESTTEST72656f440fsklearn.tree._classes.DecisionTreeClassifier (1)
A decision tree classifier.
1 runs0 likes0 downloads0 reach0 impact
TESTbdf44fa5bcsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.svm._classes.SVC)) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TESTb21546a74fsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TEST26f90dc886sklearn.linear_model._logistic.LogisticRegression (1)
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
1 runs0 likes0 downloads0 reach0 impact
TESTd799410572sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._bagging.BaggingClassifier(estimator=sklearn.svm._classes.SVC)) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TEST8fd1c1fefcsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…
1 runs0 likes0 downloads0 reach0 impact
TESTf028c1a77bsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TEST2f207bf230sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TEST2b0ba67816sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TESTTESTe400aa7978sklearn.naive_bayes.GaussianNB (1)
Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…
1 runs0 likes0 downloads0 reach0 impact
TESTb20b147a4asklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier) (1)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
1 runs0 likes0 downloads0 reach0 impact
TESTf9f5552817sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST3596ba003bsklearn.linear_model._base.LinearRegression (1)
Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset,…
1 runs0 likes0 downloads0 reach0 impact
TEST17d7ae717esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…
1 runs0 likes0 downloads0 reach0 impact
TEST69ab1f5deesklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "predict",…
1 runs0 likes0 downloads0 reach0 impact
TESTb375c8047esklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TESTcfc17859d3sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TEST21bbdb4b62sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST998997c019sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TESTb6c2f7753asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
1 runs0 likes0 downloads0 reach0 impact
TESTaec657dc0csklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.svm._classes.SVC)) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TEST124d00097esklearn.linear_model._base.LinearRegression (1)
Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset,…
1 runs0 likes0 downloads0 reach0 impact
TESTTEST745afcb1c0sklearn.naive_bayes.GaussianNB (1)
Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…
1 runs0 likes0 downloads0 reach0 impact
TEST8aef5c5d15sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST9f9e47e01esklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)) (1)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
1 runs0 likes0 downloads0 reach0 impact
TESTa7ab6bca37sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.svm._classes.SVC)) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TESTf79559cc62sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.svm._classes.SVC)) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TESTTEST55877ff969sklearn.naive_bayes.GaussianNB (1)
Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…
1 runs0 likes0 downloads0 reach0 impact
TEST64a1102addsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…
1 runs0 likes0 downloads0 reach0 impact
TEST6fcb78ac7fsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…
1 runs0 likes0 downloads0 reach0 impact
TEST5c801c9296sklearn.linear_model._logistic.LogisticRegression (1)
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
1 runs0 likes0 downloads0 reach0 impact
TEST5f0c4c4addsklearn.linear_model._base.LinearRegression (1)
Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset,…
1 runs0 likes0 downloads0 reach0 impact
TEST92c4e99b92sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST865b399207sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…
1 runs0 likes0 downloads0 reach0 impact
TESTfd133fdd53sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST7abb62c9a0sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TESTTEST5b5c695e67sklearn.tree._classes.DecisionTreeClassifier (1)
A decision tree classifier.
1 runs0 likes0 downloads0 reach0 impact
TESTea6a3fdc5fsklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TESTd92ebd7015sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TESTfb6797b657sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "predict",…
1 runs0 likes0 downloads0 reach0 impact
TEST20f7ff5263sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…
1 runs0 likes0 downloads0 reach0 impact
TEST285785d4aasklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
1 runs0 likes0 downloads0 reach0 impact
TEST11c9e43638sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.svm._classes.SVC)) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "predict",…
1 runs0 likes0 downloads0 reach0 impact
TESTd1a9992985sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST1bff45f36esklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST90c83c10d4sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TESTa1344586d2sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST927d9da8b3sklearn.linear_model._base.LinearRegression (1)
Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset,…
1 runs0 likes0 downloads0 reach0 impact
TESTe98ab00014sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST7388c493absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TESTTEST65baf09998sklearn.naive_bayes.GaussianNB (1)
Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…
1 runs0 likes0 downloads0 reach0 impact
TEST50c61db344sklearn.linear_model._logistic.LogisticRegression (1)
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
1 runs0 likes0 downloads0 reach0 impact
TESTTEST22e6b5d957sklearn.tree._classes.DecisionTreeClassifier (1)
A decision tree classifier.
1 runs0 likes0 downloads0 reach0 impact
TEST8dfeb85f5asklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST00c375e8a8sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TESTTESTcc1aeb3ff5sklearn.naive_bayes.GaussianNB (1)
Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…
1 runs0 likes0 downloads0 reach0 impact
TEST5f5a178f41sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.svm._classes.SVC)) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
1 runs0 likes0 downloads0 reach0 impact
TEST539e941e47sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…
1 runs0 likes0 downloads0 reach0 impact
TESTbcb0cfb384sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST8daf4fd8a2sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
1 runs0 likes0 downloads0 reach0 impact
TESTa6b61fd9ddsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
1 runs0 likes0 downloads0 reach0 impact
TEST960012dfddsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
1 runs0 likes0 downloads0 reach0 impact
TEST50a74f2a9csklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
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TEST810b5e7f52sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
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TESTc7ad5ae9d7sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TEST1376e5d1d7sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TEST69016afe44sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
1 runs0 likes0 downloads0 reach0 impact
TEST1696cec587sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TEST33ea703b86sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TEST9485a94e4asklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TESTe54d217818sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
1 runs0 likes0 downloads0 reach0 impact
TEST16462b91c8sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST9ef410b5ebsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TEST0bc9beb6ddsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TESTb039f1ff48sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
TEST1f96e4821fsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TESTbb6900e37esklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
1 runs0 likes0 downloads0 reach0 impact
TEST2085ab144csklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TESTd7fec2bafasklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._bagging.BaggingClassifier(base_estimator=sklearn.svm._classes.SVC)) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
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TEST670adb8dd2sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._bagging.BaggingClassifier(estimator=sklearn.svm._classes.SVC)) (1)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples",…
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TEST00d23a08ebsklearn.linear_model._logistic.LogisticRegression (1)
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
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TEST82f24558f0sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
TEST2459d9bcc3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…
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