Update README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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David Rotermund
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1 changed files with 98 additions and 290 deletions
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@ -941,190 +941,97 @@ Time Series cross-validator
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model_selection.check_cv([cv, y, classifier])
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Input checker utility for building a cross-validator.
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model_selection.train_test_split(*arrays[, ...])
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Split arrays or matrices into random train and test subsets.
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|model_selection.check_cv([cv, y, classifier])|Input checker utility for building a cross-validator.|
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|model_selection.train_test_split(*arrays[, ...])|Split arrays or matrices into random train and test subsets.|
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### Hyper-parameter optimizers
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model_selection.GridSearchCV(estimator, ...)
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Exhaustive search over specified parameter values for an estimator.
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model_selection.HalvingGridSearchCV(...[, ...])
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Search over specified parameter values with successive halving.
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model_selection.ParameterGrid(param_grid)
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Grid of parameters with a discrete number of values for each.
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model_selection.ParameterSampler(...[, ...])
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Generator on parameters sampled from given distributions.
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model_selection.RandomizedSearchCV(...[, ...])
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Randomized search on hyper parameters.
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model_selection.HalvingRandomSearchCV(...[, ...])
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Randomized search on hyper parameters.
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|model_selection.GridSearchCV(estimator, ...)|Exhaustive search over specified parameter values for an estimator.|
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|model_selection.HalvingGridSearchCV(...[, ...])|Search over specified parameter values with successive halving.|
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|model_selection.ParameterGrid(param_grid)|Grid of parameters with a discrete number of values for each.|
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|model_selection.ParameterSampler(...[, ...])|Generator on parameters sampled from given distributions.|
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|model_selection.RandomizedSearchCV(...[, ...])|Randomized search on hyper parameters.|
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|model_selection.HalvingRandomSearchCV(...[, ...])|Randomized search on hyper parameters.|
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### Model validation
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model_selection.cross_validate(estimator, X)
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Evaluate metric(s) by cross-validation and also record fit/score times.
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model_selection.cross_val_predict(estimator, X)
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Generate cross-validated estimates for each input data point.
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model_selection.cross_val_score(estimator, X)
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Evaluate a score by cross-validation.
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model_selection.learning_curve(estimator, X, ...)
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Learning curve.
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model_selection.permutation_test_score(...)
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Evaluate the significance of a cross-validated score with permutations.
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model_selection.validation_curve(estimator, ...)
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Validation curve.
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|model_selection.cross_validate(estimator, X)|Evaluate metric(s) by cross-validation and also record fit/score times.|
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|model_selection.cross_val_predict(estimator, X)|Generate cross-validated estimates for each input data point.|
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|model_selection.cross_val_score(estimator, X)|Evaluate a score by cross-validation.|
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|model_selection.learning_curve(estimator, X, ...)|Learning curve.|
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|model_selection.permutation_test_score(...)|Evaluate the significance of a cross-validated score with permutations.|
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|model_selection.validation_curve(estimator, ...)|Validation curve.|
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### Visualization
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model_selection.LearningCurveDisplay(*, ...)
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Learning Curve visualization.
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model_selection.ValidationCurveDisplay(*, ...)
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Validation Curve visualization.
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|model_selection.LearningCurveDisplay(*, ...)|Learning Curve visualization.|
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|model_selection.ValidationCurveDisplay(*, ...)|Validation Curve visualization.|
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## [sklearn.multiclass: Multiclass classification](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.multiclass)
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multiclass.OneVsRestClassifier(estimator, *)
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One-vs-the-rest (OvR) multiclass strategy.
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multiclass.OneVsOneClassifier(estimator, *)
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One-vs-one multiclass strategy.
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multiclass.OutputCodeClassifier(estimator, *)
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(Error-Correcting) Output-Code multiclass strategy.
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|multiclass.OneVsRestClassifier(estimator, *)|One-vs-the-rest (OvR) multiclass strategy.|
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|multiclass.OneVsOneClassifier(estimator, *)|One-vs-one multiclass strategy.|
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|multiclass.OutputCodeClassifier(estimator, *)|(Error-Correcting) Output-Code multiclass strategy.|
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## [sklearn.multioutput: Multioutput regression and classification](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.multioutput)
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multioutput.ClassifierChain(base_estimator, *)
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A multi-label model that arranges binary classifiers into a chain.
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multioutput.MultiOutputRegressor(estimator, *)
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Multi target regression.
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multioutput.MultiOutputClassifier(estimator, *)
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Multi target classification.
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multioutput.RegressorChain(base_estimator, *)
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A multi-label model that arranges regressions into a chain.
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|multioutput.ClassifierChain(base_estimator, *)|A multi-label model that arranges binary classifiers into a chain.|
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|multioutput.MultiOutputRegressor(estimator, *)|Multi target regression.|
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|multioutput.MultiOutputClassifier(estimator, *)|Multi target classification.|
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|multioutput.RegressorChain(base_estimator, *)|A multi-label model that arranges regressions into a chain.|
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## [sklearn.naive_bayes: Naive Bayes](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.naive_bayes)
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naive_bayes.BernoulliNB(*[, alpha, ...])
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Naive Bayes classifier for multivariate Bernoulli models.
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naive_bayes.CategoricalNB(*[, alpha, ...])
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Naive Bayes classifier for categorical features.
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naive_bayes.ComplementNB(*[, alpha, ...])
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The Complement Naive Bayes classifier described in Rennie et al. (2003).
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naive_bayes.GaussianNB(*[, priors, ...])
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Gaussian Naive Bayes (GaussianNB).
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naive_bayes.MultinomialNB(*[, alpha, ...])
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Naive Bayes classifier for multinomial models.
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|naive_bayes.BernoulliNB(*[, alpha, ...])|Naive Bayes classifier for multivariate Bernoulli models.|
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|naive_bayes.CategoricalNB(*[, alpha, ...])|Naive Bayes classifier for categorical features.|
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|naive_bayes.ComplementNB(*[, alpha, ...])|The Complement Naive Bayes classifier described in Rennie et al. (2003).|
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|naive_bayes.GaussianNB(*[, priors, ...])|Gaussian Naive Bayes (GaussianNB).|
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|naive_bayes.MultinomialNB(*[, alpha, ...])|Naive Bayes classifier for multinomial models.|
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## [sklearn.neighbors: Nearest Neighbors](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors)
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neighbors.BallTree(X[, leaf_size, metric])
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BallTree for fast generalized N-point problems
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neighbors.KDTree(X[, leaf_size, metric])
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KDTree for fast generalized N-point problems
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neighbors.KernelDensity(*[, bandwidth, ...])
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Kernel Density Estimation.
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**neighbors.KNeighborsClassifier([...])**
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**Classifier implementing the k-nearest neighbors vote.**
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neighbors.KNeighborsRegressor([n_neighbors, ...])
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Regression based on k-nearest neighbors.
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neighbors.KNeighborsTransformer(*[, mode, ...])
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Transform X into a (weighted) graph of k nearest neighbors.
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neighbors.LocalOutlierFactor([n_neighbors, ...])
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Unsupervised Outlier Detection using the Local Outlier Factor (LOF).
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neighbors.RadiusNeighborsClassifier([...])
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Classifier implementing a vote among neighbors within a given radius.
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neighbors.RadiusNeighborsRegressor([radius, ...])
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Regression based on neighbors within a fixed radius.
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neighbors.RadiusNeighborsTransformer(*[, ...])
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Transform X into a (weighted) graph of neighbors nearer than a radius.
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neighbors.NearestCentroid([metric, ...])
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Nearest centroid classifier.
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neighbors.NearestNeighbors(*[, n_neighbors, ...])
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Unsupervised learner for implementing neighbor searches.
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neighbors.NeighborhoodComponentsAnalysis([...])
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Neighborhood Components Analysis.
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neighbors.kneighbors_graph(X, n_neighbors, *)
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Compute the (weighted) graph of k-Neighbors for points in X.
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neighbors.radius_neighbors_graph(X, radius, *)
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Compute the (weighted) graph of Neighbors for points in X.
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neighbors.sort_graph_by_row_values(graph[, ...])
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Sort a sparse graph such that each row is stored with increasing values.
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|neighbors.BallTree(X[, leaf_size, metric])|BallTree for fast generalized N-point problems|
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|neighbors.KDTree(X[, leaf_size, metric])|KDTree for fast generalized N-point problems|
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|neighbors.KernelDensity(*[, bandwidth, ...])|Kernel Density Estimation.|
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|**neighbors.KNeighborsClassifier([...])**|**Classifier implementing the k-nearest neighbors vote.**|
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|neighbors.KNeighborsRegressor([n_neighbors, ...])|Regression based on k-nearest neighbors.|
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|neighbors.KNeighborsTransformer(*[, mode, ...])|Transform X into a (weighted) graph of k nearest neighbors.|
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|neighbors.LocalOutlierFactor([n_neighbors, ...])|Unsupervised Outlier Detection using the Local Outlier Factor (LOF).|
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|neighbors.RadiusNeighborsClassifier([...])|Classifier implementing a vote among neighbors within a given radius.|
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|neighbors.RadiusNeighborsRegressor([radius, ...])|Regression based on neighbors within a fixed radius.|
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|neighbors.RadiusNeighborsTransformer(*[, ...])|Transform X into a (weighted) graph of neighbors nearer than a radius.|
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|neighbors.NearestCentroid([metric, ...])|Nearest centroid classifier.|
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|neighbors.NearestNeighbors(*[, n_neighbors, ...])|Unsupervised learner for implementing neighbor searches.|
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|neighbors.NeighborhoodComponentsAnalysis([...])|Neighborhood Components Analysis.|
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|neighbors.kneighbors_graph(X, n_neighbors, *)|Compute the (weighted) graph of k-Neighbors for points in X.|
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|neighbors.radius_neighbors_graph(X, radius, *)|Compute the (weighted) graph of Neighbors for points in X.|
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|neighbors.sort_graph_by_row_values(graph[, ...])|Sort a sparse graph such that each row is stored with increasing values.|
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## [sklearn.neural_network: Neural network models](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neural_network)
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pipeline.FeatureUnion(transformer_list, *[, ...])
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Concatenates results of multiple transformer objects.
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pipeline.Pipeline(steps, *[, memory, verbose])
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Pipeline of transforms with a final estimator.
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pipeline.make_pipeline(*steps[, memory, verbose])
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Construct a Pipeline from the given estimators.
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pipeline.make_union(*transformers[, n_jobs, ...])
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Construct a FeatureUnion from the given transformers.
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|pipeline.FeatureUnion(transformer_list, *[, ...])|Concatenates results of multiple transformer objects.|
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|pipeline.Pipeline(steps, *[, memory, verbose])|Pipeline of transforms with a final estimator.|
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|pipeline.make_pipeline(*steps[, memory, verbose])|Construct a Pipeline from the given estimators.|
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|pipeline.make_union(*transformers[, n_jobs, ...])|Construct a FeatureUnion from the given transformers.|
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## [sklearn.pipeline: Pipeline](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.pipeline)
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@ -1135,177 +1042,78 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.p
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preprocessing.Binarizer(*[, threshold, copy])
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Binarize data (set feature values to 0 or 1) according to a threshold.
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preprocessing.FunctionTransformer([func, ...])
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Constructs a transformer from an arbitrary callable.
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preprocessing.KBinsDiscretizer([n_bins, ...])
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Bin continuous data into intervals.
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preprocessing.KernelCenterer()
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Center an arbitrary kernel matrix
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preprocessing.LabelBinarizer(*[, neg_label, ...])
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Binarize labels in a one-vs-all fashion.
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preprocessing.LabelEncoder()
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Encode target labels with value between 0 and n_classes-1.
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preprocessing.MultiLabelBinarizer(*[, ...])
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Transform between iterable of iterables and a multilabel format.
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preprocessing.MaxAbsScaler(*[, copy])
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Scale each feature by its maximum absolute value.
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preprocessing.MinMaxScaler([feature_range, ...])
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Transform features by scaling each feature to a given range.
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preprocessing.Normalizer([norm, copy])
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Normalize samples individually to unit norm.
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preprocessing.OneHotEncoder(*[, categories, ...])
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Encode categorical features as a one-hot numeric array.
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preprocessing.OrdinalEncoder(*[, ...])
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Encode categorical features as an integer array.
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preprocessing.PolynomialFeatures([degree, ...])
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Generate polynomial and interaction features.
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preprocessing.PowerTransformer([method, ...])
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Apply a power transform featurewise to make data more Gaussian-like.
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preprocessing.QuantileTransformer(*[, ...])
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Transform features using quantiles information.
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preprocessing.RobustScaler(*[, ...])
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Scale features using statistics that are robust to outliers.
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preprocessing.SplineTransformer([n_knots, ...])
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Generate univariate B-spline bases for features.
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preprocessing.StandardScaler(*[, copy, ...])
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Standardize features by removing the mean and scaling to unit variance.
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preprocessing.TargetEncoder([categories, ...])
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Target Encoder for regression and classification targets.
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preprocessing.add_dummy_feature(X[, value])
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Augment dataset with an additional dummy feature.
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preprocessing.binarize(X, *[, threshold, copy])
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Boolean thresholding of array-like or scipy.sparse matrix.
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preprocessing.label_binarize(y, *, classes)
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Binarize labels in a one-vs-all fashion.
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preprocessing.maxabs_scale(X, *[, axis, copy])
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Scale each feature to the [-1, 1] range without breaking the sparsity.
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preprocessing.minmax_scale(X[, ...])
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Transform features by scaling each feature to a given range.
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preprocessing.normalize(X[, norm, axis, ...])
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Scale input vectors individually to unit norm (vector length).
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preprocessing.quantile_transform(X, *[, ...])
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Transform features using quantiles information.
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preprocessing.robust_scale(X, *[, axis, ...])
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Standardize a dataset along any axis.
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preprocessing.scale(X, *[, axis, with_mean, ...])
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Standardize a dataset along any axis.
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preprocessing.power_transform(X[, method, ...])
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Parametric, monotonic transformation to make data more Gaussian-like.
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|preprocessing.Binarizer(*[, threshold, copy])|Binarize data (set feature values to 0 or 1) according to a threshold.|
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|preprocessing.FunctionTransformer([func, ...])|Constructs a transformer from an arbitrary callable.|
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|preprocessing.KBinsDiscretizer([n_bins, ...])|Bin continuous data into intervals.|
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|preprocessing.KernelCenterer()|Center an arbitrary kernel matrix |
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|preprocessing.LabelBinarizer(*[, neg_label, ...])|Binarize labels in a one-vs-all fashion.|
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|preprocessing.LabelEncoder()|Encode target labels with value between 0 and n_classes-1.v
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|preprocessing.MultiLabelBinarizer(*[, ...])|Transform between iterable of iterables and a multilabel format.|
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|preprocessing.MaxAbsScaler(*[, copy])|Scale each feature by its maximum absolute value.|
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|preprocessing.MinMaxScaler([feature_range, ...])|Transform features by scaling each feature to a given range.|
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|preprocessing.Normalizer([norm, copy])|Normalize samples individually to unit norm.|
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|preprocessing.OneHotEncoder(*[, categories, ...])|Encode categorical features as a one-hot numeric array.|
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|preprocessing.OrdinalEncoder(*[, ...])|Encode categorical features as an integer array.|
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|preprocessing.PolynomialFeatures([degree, ...])|Generate polynomial and interaction features.|
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|preprocessing.PowerTransformer([method, ...])|Apply a power transform featurewise to make data more Gaussian-like.|
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|preprocessing.QuantileTransformer(*[, ...])|Transform features using quantiles information.|
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|preprocessing.RobustScaler(*[, ...])|Scale features using statistics that are robust to outliers.|
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|preprocessing.SplineTransformer([n_knots, ...])|Generate univariate B-spline bases for features.|
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|preprocessing.StandardScaler(*[, copy, ...])|Standardize features by removing the mean and scaling to unit variance.|
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|preprocessing.TargetEncoder([categories, ...])|Target Encoder for regression and classification targets.|
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|preprocessing.add_dummy_feature(X[, value])|Augment dataset with an additional dummy feature.|
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|preprocessing.binarize(X, *[, threshold, copy])|Boolean thresholding of array-like or scipy.sparse matrix.|
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|preprocessing.label_binarize(y, *, classes)|Binarize labels in a one-vs-all fashion.|
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|preprocessing.maxabs_scale(X, *[, axis, copy])|Scale each feature to the [-1, 1] range without breaking the sparsity.|
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|preprocessing.minmax_scale(X[, ...])|Transform features by scaling each feature to a given range.|
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|preprocessing.normalize(X[, norm, axis, ...])|Scale input vectors individually to unit norm (vector length).|
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|preprocessing.quantile_transform(X, *[, ...])|Transform features using quantiles information.|
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|preprocessing.robust_scale(X, *[, axis, ...])|Standardize a dataset along any axis.|
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|preprocessing.scale(X, *[, axis, with_mean, ...])|Standardize a dataset along any axis.|
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|preprocessing.power_transform(X[, method, ...])|Parametric, monotonic transformation to make data more Gaussian-like.|
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## [sklearn.random_projection: Random projection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.random_projection)
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random_projection.GaussianRandomProjection([...])
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Reduce dimensionality through Gaussian random projection.
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random_projection.SparseRandomProjection([...])
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Reduce dimensionality through sparse random projection.
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random_projection.johnson_lindenstrauss_min_dim(...)
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Find a 'safe' number of components to randomly project to.
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|random_projection.GaussianRandomProjection([...])|Reduce dimensionality through Gaussian random projection.|
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|random_projection.SparseRandomProjection([...])|Reduce dimensionality through sparse random projection.|
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|random_projection.johnson_lindenstrauss_min_dim(...)|Find a 'safe' number of components to randomly project to.|
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## [sklearn.semi_supervised: Semi-Supervised Learning](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.semi_supervised)
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semi_supervised.LabelPropagation([kernel, ...])
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Label Propagation classifier.
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semi_supervised.LabelSpreading([kernel, ...])
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LabelSpreading model for semi-supervised learning.
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semi_supervised.SelfTrainingClassifier(...)
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Self-training classifier.
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|semi_supervised.LabelPropagation([kernel, ...])|Label Propagation classifier.|
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|semi_supervised.LabelSpreading([kernel, ...])|LabelSpreading model for semi-supervised learning.|
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|semi_supervised.SelfTrainingClassifier(...)|Self-training classifier.|
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## [sklearn.svm: Support Vector Machines](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm)
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svm.LinearSVC([penalty, loss, dual, tol, C, ...])
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Linear Support Vector Classification.
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|
||||
svm.LinearSVR(*[, epsilon, tol, C, loss, ...])
|
||||
Linear Support Vector Regression.
|
||||
|
||||
svm.NuSVC(*[, nu, kernel, degree, gamma, ...])
|
||||
Nu-Support Vector Classification.
|
||||
|
||||
svm.NuSVR(*[, nu, C, kernel, degree, gamma, ...])
|
||||
Nu Support Vector Regression.
|
||||
|
||||
svm.OneClassSVM(*[, kernel, degree, gamma, ...])
|
||||
Unsupervised Outlier Detection.
|
||||
|
||||
svm.SVC(*[, C, kernel, degree, gamma, ...])
|
||||
C-Support Vector Classification.
|
||||
|
||||
svm.SVR(*[, kernel, degree, gamma, coef0, ...])
|
||||
Epsilon-Support Vector Regression.
|
||||
|
||||
svm.l1_min_c(X, y, *[, loss, fit_intercept, ...])
|
||||
Return the lowest bound for C.
|
||||
|
||||
|svm.LinearSVC([penalty, loss, dual, tol, C, ...])|Linear Support Vector Classification.|
|
||||
|svm.LinearSVR(*[, epsilon, tol, C, loss, ...])|Linear Support Vector Regression.|
|
||||
|svm.NuSVC(*[, nu, kernel, degree, gamma, ...])|Nu-Support Vector Classification.|
|
||||
|svm.NuSVR(*[, nu, C, kernel, degree, gamma, ...])|Nu Support Vector Regression.|
|
||||
|svm.OneClassSVM(*[, kernel, degree, gamma, ...])|Unsupervised Outlier Detection.|
|
||||
|svm.SVC(*[, C, kernel, degree, gamma, ...])|C-Support Vector Classification.|
|
||||
|svm.SVR(*[, kernel, degree, gamma, coef0, ...])|Epsilon-Support Vector Regression.|
|
||||
|svm.l1_min_c(X, y, *[, loss, fit_intercept, ...])|Return the lowest bound for C.|
|
||||
|
||||
|
||||
## [sklearn.tree: Decision Trees](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree)
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
tree.DecisionTreeClassifier(*[, criterion, ...])
|
||||
A decision tree classifier.
|
||||
|
||||
tree.DecisionTreeRegressor(*[, criterion, ...])
|
||||
A decision tree regressor.
|
||||
|
||||
tree.ExtraTreeClassifier(*[, criterion, ...])
|
||||
An extremely randomized tree classifier.
|
||||
|
||||
tree.ExtraTreeRegressor(*[, criterion, ...])
|
||||
An extremely randomized tree regressor.
|
||||
|
||||
tree.export_graphviz(decision_tree[, ...])
|
||||
Export a decision tree in DOT format.
|
||||
|
||||
tree.export_text(decision_tree, *[, ...])
|
||||
Build a text report showing the rules of a decision tree.
|
||||
|
||||
tree.plot_tree(decision_tree, *[, ...])
|
||||
Plot a decision tree.
|
||||
|tree.DecisionTreeClassifier(*[, criterion, ...])|A decision tree classifier.|
|
||||
|tree.DecisionTreeRegressor(*[, criterion, ...])|A decision tree regressor.|
|
||||
|tree.ExtraTreeClassifier(*[, criterion, ...])|An extremely randomized tree classifier.|
|
||||
|tree.ExtraTreeRegressor(*[, criterion, ...])|An extremely randomized tree regressor.|
|
||||
|tree.export_graphviz(decision_tree[, ...])|Export a decision tree in DOT format.|
|
||||
|tree.export_text(decision_tree, *[, ...])|Build a text report showing the rules of a decision tree.|
|
||||
|tree.plot_tree(decision_tree, *[, ...])|Plot a decision tree.|
|
||||
|
||||
## [sklearn.utils: Utilities](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.utils)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue