Flow
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
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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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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
DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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
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",…
0 runs0 likes0 downloads0 reach0 impact
DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.
0 runs0 likes0 downloads0 reach0 impact
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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
DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.
0 runs0 likes0 downloads0 reach0 impact
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,…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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
DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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,…
0 runs0 likes0 downloads0 reach0 impact
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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
DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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
DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
0 runs0 likes0 downloads0 reach0 impact
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
0 runs0 likes0 downloads0 reach0 impact
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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,…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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
DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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
DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
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",…
0 runs0 likes0 downloads0 reach0 impact