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sklearn.ensemble._forest.RandomForestClassifier (11)
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
8 runs0 likes0 downloads0 reach5 impact
sklearn.ensemble._voting.VotingClassifier(svm=sklearn.svm._classes.SVC,rf=sklearn.ensemble._forest.RandomForestClassifier,lr=sklearn.linear_model._logistic.LogisticRegression) (1)
Soft Voting/Majority Rule classifier for unfitted estimators.
0 runs0 likes0 downloads0 reach0 impact
sklearn.svm._classes.SVC (9)
C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…
1 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model._logistic.LogisticRegression (6)
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…
2 runs0 likes0 downloads0 reach2 impact
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer,std_scale=sklearn.preprocessing._data.StandardScaler,svr=sklearn.svm._classes.SVR) (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
sklearn.preprocessing._data.StandardScaler (9)
Standardize features by removing the mean and scaling to unit variance The standard score of a sample `x` is calculated as: z = (x - u) / s where `u` is the mean of the training samples or zero if…
0 runs0 likes0 downloads0 reach0 impact
sklearn.svm._classes.SVR (2)
Epsilon-Support Vector Regression. The free parameters in the model are C and epsilon. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer,std_scale=sklearn.preprocessing._data.StandardScaler,knn=sklearn.neighbors._regression.KNeighborsRegressor) (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
sklearn.neighbors._regression.KNeighborsRegressor (3)
Regression based on k-nearest neighbors. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(StandardScaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC) (4)
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…
4 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(MinMaxScaler=sklearn.preprocessing._data.MinMaxScaler,svc=sklearn.svm._classes.SVC) (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…
2 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing._data.MinMaxScaler (3)
Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder,rf=sklearn.ensemble._forest.RandomForestClassifier) (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
sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OrdinalEncoder=sklearn.preprocessing._encoders.OrdinalEncoder,rf=sklearn.ensemble._forest.RandomForestClassifier) (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
sklearn.preprocessing._encoders.OrdinalEncoder (3)
Encode categorical features as an integer array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OrdinalEncoder=sklearn.preprocessing._encoders.OrdinalEncoder,vc=sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=sklearn.svm._classes.SVC)) (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
sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=sklearn.svm._classes.SVC) (1)
Soft Voting/Majority Rule classifier for unfitted estimators.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder),MaxAbsScaler=sklearn.preprocessing._data.MaxAbsScaler,svc=sklearn.svm._classes.SVC) (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
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder) (1)
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each…
0 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing._data.MaxAbsScaler (2)
Scale each feature by its maximum absolute value. This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0.…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder),hist=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (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
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier (8)
Histogram-based Gradient Boosting Classification Tree. This estimator is much faster than :class:`GradientBoostingClassifier` for big datasets (n_samples…
0 runs0 likes0 downloads0 reach0 impact