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@ -41,7 +41,9 @@ Compute true and predicted probabilities for a calibration curve.
## [sklearn.cluster: Clustering](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster)
see more [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster)
### Classes
cluster.AffinityPropagation(*[, damping, ...])
Perform Affinity Propagation Clustering of data.
@ -85,6 +87,41 @@ Spectral biclustering (Kluger, 2003).
cluster.SpectralCoclustering([n_clusters, ...])
Spectral Co-Clustering algorithm (Dhillon, 2001).
### Functions
cluster.affinity_propagation(S, *[, ...])
Perform Affinity Propagation Clustering of data.
cluster.cluster_optics_dbscan(*, ...)
Perform DBSCAN extraction for an arbitrary epsilon.
cluster.cluster_optics_xi(*, reachability, ...)
Automatically extract clusters according to the Xi-steep method.
cluster.compute_optics_graph(X, *, ...)
Compute the OPTICS reachability graph.
cluster.dbscan(X[, eps, min_samples, ...])
Perform DBSCAN clustering from vector array or distance matrix.
cluster.estimate_bandwidth(X, *[, quantile, ...])
Estimate the bandwidth to use with the mean-shift algorithm.
cluster.k_means(X, n_clusters, *[, ...])
Perform K-means clustering algorithm.
cluster.kmeans_plusplus(X, n_clusters, *[, ...])
Init n_clusters seeds according to k-means++.
cluster.mean_shift(X, *[, bandwidth, seeds, ...])
Perform mean shift clustering of data using a flat kernel.
cluster.spectral_clustering(affinity, *[, ...])
Apply clustering to a projection of the normalized Laplacian.
cluster.ward_tree(X, *[, connectivity, ...])
Ward clustering based on a Feature matrix.
## [sklearn.compose: Composite Estimators](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.compose)
@ -306,6 +343,765 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.e
see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.experimental)
## [sklearn.feature_extraction: Feature Extraction](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_extraction)
feature_extraction.DictVectorizer(*[, ...])
Transforms lists of feature-value mappings to vectors.
feature_extraction.FeatureHasher([...])
Implements feature hashing, aka the hashing trick.
### From images
feature_extraction.image.extract_patches_2d(...)
Reshape a 2D image into a collection of patches.
feature_extraction.image.grid_to_graph(n_x, n_y)
Graph of the pixel-to-pixel connections.
feature_extraction.image.img_to_graph(img, *)
Graph of the pixel-to-pixel gradient connections.
feature_extraction.image.reconstruct_from_patches_2d(...)
Reconstruct the image from all of its patches.
feature_extraction.image.PatchExtractor(*[, ...])
Extracts patches from a collection of images.
### From text
feature_extraction.text.CountVectorizer(*[, ...])
Convert a collection of text documents to a matrix of token counts.
feature_extraction.text.HashingVectorizer(*)
Convert a collection of text documents to a matrix of token occurrences.
feature_extraction.text.TfidfTransformer(*)
Transform a count matrix to a normalized tf or tf-idf representation.
feature_extraction.text.TfidfVectorizer(*[, ...])
Convert a collection of raw documents to a matrix of TF-IDF features.
## [sklearn.feature_selection: Feature Selection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection)
feature_selection.GenericUnivariateSelect([...])
Univariate feature selector with configurable strategy.
feature_selection.SelectPercentile([...])
Select features according to a percentile of the highest scores.
feature_selection.SelectKBest([score_func, k])
Select features according to the k highest scores.
feature_selection.SelectFpr([score_func, alpha])
Filter: Select the pvalues below alpha based on a FPR test.
feature_selection.SelectFdr([score_func, alpha])
Filter: Select the p-values for an estimated false discovery rate.
feature_selection.SelectFromModel(estimator, *)
Meta-transformer for selecting features based on importance weights.
feature_selection.SelectFwe([score_func, alpha])
Filter: Select the p-values corresponding to Family-wise error rate.
feature_selection.SequentialFeatureSelector(...)
Transformer that performs Sequential Feature Selection.
feature_selection.RFE(estimator, *[, ...])
Feature ranking with recursive feature elimination.
feature_selection.RFECV(estimator, *[, ...])
Recursive feature elimination with cross-validation to select features.
feature_selection.VarianceThreshold([threshold])
Feature selector that removes all low-variance features.
feature_selection.chi2(X, y)
Compute chi-squared stats between each non-negative feature and class.
feature_selection.f_classif(X, y)
Compute the ANOVA F-value for the provided sample.
feature_selection.f_regression(X, y, *[, ...])
Univariate linear regression tests returning F-statistic and p-values.
feature_selection.r_regression(X, y, *[, ...])
Compute Pearson's r for each features and the target.
feature_selection.mutual_info_classif(X, y, *)
Estimate mutual information for a discrete target variable.
feature_selection.mutual_info_regression(X, y, *)
Estimate mutual information for a continuous target variable.
## [sklearn.gaussian_process: Gaussian Processes]()
gaussian_process.GaussianProcessClassifier([...])
Gaussian process classification (GPC) based on Laplace approximation.
gaussian_process.GaussianProcessRegressor([...])
Gaussian process regression (GPR).
### Kernels
gaussian_process.kernels.CompoundKernel(kernels)
Kernel which is composed of a set of other kernels.
gaussian_process.kernels.ConstantKernel([...])
Constant kernel.
gaussian_process.kernels.DotProduct([...])
Dot-Product kernel.
gaussian_process.kernels.ExpSineSquared([...])
Exp-Sine-Squared kernel (aka periodic kernel).
gaussian_process.kernels.Exponentiation(...)
The Exponentiation kernel takes one base kernel and a scalar parameter and combines them via
gaussian_process.kernels.Hyperparameter(...)
A kernel hyperparameter's specification in form of a namedtuple.
gaussian_process.kernels.Kernel()
Base class for all kernels.
gaussian_process.kernels.Matern([...])
Matern kernel.
gaussian_process.kernels.PairwiseKernel([...])
Wrapper for kernels in sklearn.metrics.pairwise.
gaussian_process.kernels.Product(k1, k2)
The Product kernel takes two kernels k1 and k2 and combines them via
gaussian_process.kernels.RBF([length_scale, ...])
Radial basis function kernel (aka squared-exponential kernel).
gaussian_process.kernels.RationalQuadratic([...])
Rational Quadratic kernel.
gaussian_process.kernels.Sum(k1, k2)
The Sum kernel takes two kernels k1 and k2 and combines them via
gaussian_process.kernels.WhiteKernel([...])
White kernel.
## [sklearn.impute: Impute](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute)
impute.SimpleImputer(*[, missing_values, ...])
Univariate imputer for completing missing values with simple strategies.
impute.IterativeImputer([estimator, ...])
Multivariate imputer that estimates each feature from all the others.
impute.MissingIndicator(*[, missing_values, ...])
Binary indicators for missing values.
impute.KNNImputer(*[, missing_values, ...])
Imputation for completing missing values using k-Nearest Neighbors.
## [sklearn.inspection: Inspection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.inspection)
inspection.partial_dependence(estimator, X, ...)
Partial dependence of features.
inspection.permutation_importance(estimator, ...)
Permutation importance for feature evaluation [Rd9e56ef97513-BRE].
### Plotting
inspection.DecisionBoundaryDisplay(*, xx0, ...)
Decisions boundary visualization.
inspection.PartialDependenceDisplay(...[, ...])
Partial Dependence Plot (PDP).
## [sklearn.isotonic: Isotonic regression](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.isotonic)
isotonic.IsotonicRegression(*[, y_min, ...])
Isotonic regression model.
isotonic.check_increasing(x, y)
Determine whether y is monotonically correlated with x.
isotonic.isotonic_regression(y, *[, ...])
Solve the isotonic regression model.
## [sklearn.kernel_approximation: Kernel Approximation](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_approximation)
kernel_approximation.AdditiveChi2Sampler(*)
Approximate feature map for additive chi2 kernel.
kernel_approximation.Nystroem([kernel, ...])
Approximate a kernel map using a subset of the training data.
kernel_approximation.PolynomialCountSketch(*)
Polynomial kernel approximation via Tensor Sketch.
kernel_approximation.RBFSampler(*[, gamma, ...])
Approximate a RBF kernel feature map using random Fourier features.
kernel_approximation.SkewedChi2Sampler(*[, ...])
Approximate feature map for "skewed chi-squared" kernel.
# ---------------
## [sklearn.metrics: Metrics](https://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics)
### Model Selection Interface
metrics.check_scoring(estimator[, scoring, ...])
Determine scorer from user options.
metrics.get_scorer(scoring)
Get a scorer from string.
metrics.get_scorer_names()
Get the names of all available scorers.
metrics.make_scorer(score_func, *[, ...])
Make a scorer from a performance metric or loss function.
### Classification metrics
metrics.accuracy_score(y_true, y_pred, *[, ...])
Accuracy classification score.
metrics.auc(x, y)
Compute Area Under the Curve (AUC) using the trapezoidal rule.
metrics.average_precision_score(y_true, ...)
Compute average precision (AP) from prediction scores.
metrics.balanced_accuracy_score(y_true, ...)
Compute the balanced accuracy.
metrics.brier_score_loss(y_true, y_prob, *)
Compute the Brier score loss.
metrics.class_likelihood_ratios(y_true, ...)
Compute binary classification positive and negative likelihood ratios.
metrics.classification_report(y_true, y_pred, *)
Build a text report showing the main classification metrics.
metrics.cohen_kappa_score(y1, y2, *[, ...])
Compute Cohen's kappa: a statistic that measures inter-annotator agreement.
metrics.confusion_matrix(y_true, y_pred, *)
Compute confusion matrix to evaluate the accuracy of a classification.
metrics.dcg_score(y_true, y_score, *[, k, ...])
Compute Discounted Cumulative Gain.
metrics.det_curve(y_true, y_score[, ...])
Compute error rates for different probability thresholds.
metrics.f1_score(y_true, y_pred, *[, ...])
Compute the F1 score, also known as balanced F-score or F-measure.
metrics.fbeta_score(y_true, y_pred, *, beta)
Compute the F-beta score.
metrics.hamming_loss(y_true, y_pred, *[, ...])
Compute the average Hamming loss.
metrics.hinge_loss(y_true, pred_decision, *)
Average hinge loss (non-regularized).
metrics.jaccard_score(y_true, y_pred, *[, ...])
Jaccard similarity coefficient score.
metrics.log_loss(y_true, y_pred, *[, eps, ...])
Log loss, aka logistic loss or cross-entropy loss.
metrics.matthews_corrcoef(y_true, y_pred, *)
Compute the Matthews correlation coefficient (MCC).
metrics.multilabel_confusion_matrix(y_true, ...)
Compute a confusion matrix for each class or sample.
metrics.ndcg_score(y_true, y_score, *[, k, ...])
Compute Normalized Discounted Cumulative Gain.
metrics.precision_recall_curve(y_true, ...)
Compute precision-recall pairs for different probability thresholds.
metrics.precision_recall_fscore_support(...)
Compute precision, recall, F-measure and support for each class.
metrics.precision_score(y_true, y_pred, *[, ...])
Compute the precision.
metrics.recall_score(y_true, y_pred, *[, ...])
Compute the recall.
metrics.roc_auc_score(y_true, y_score, *[, ...])
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
metrics.roc_curve(y_true, y_score, *[, ...])
Compute Receiver operating characteristic (ROC).
metrics.top_k_accuracy_score(y_true, y_score, *)
Top-k Accuracy classification score.
metrics.zero_one_loss(y_true, y_pred, *[, ...])
Zero-one classification loss.
### Regression metrics
metrics.explained_variance_score(y_true, ...)
Explained variance regression score function.
metrics.max_error(y_true, y_pred)
The max_error metric calculates the maximum residual error.
metrics.mean_absolute_error(y_true, y_pred, *)
Mean absolute error regression loss.
metrics.mean_squared_error(y_true, y_pred, *)
Mean squared error regression loss.
metrics.mean_squared_log_error(y_true, y_pred, *)
Mean squared logarithmic error regression loss.
metrics.median_absolute_error(y_true, y_pred, *)
Median absolute error regression loss.
metrics.mean_absolute_percentage_error(...)
Mean absolute percentage error (MAPE) regression loss.
metrics.r2_score(y_true, y_pred, *[, ...])
R^2 (coefficient of determination) regression score function.
metrics.mean_poisson_deviance(y_true, y_pred, *)
Mean Poisson deviance regression loss.
metrics.mean_gamma_deviance(y_true, y_pred, *)
Mean Gamma deviance regression loss.
metrics.mean_tweedie_deviance(y_true, y_pred, *)
Mean Tweedie deviance regression loss.
metrics.d2_tweedie_score(y_true, y_pred, *)
D^2 regression score function, fraction of Tweedie deviance explained.
metrics.mean_pinball_loss(y_true, y_pred, *)
Pinball loss for quantile regression.
metrics.d2_pinball_score(y_true, y_pred, *)
D^2 regression score function, fraction of pinball loss explained.
metrics.d2_absolute_error_score(y_true, ...)
D^2 regression score function, fraction of absolute error explained.
### Multilabel ranking metrics
metrics.coverage_error(y_true, y_score, *[, ...])
Coverage error measure.
metrics.label_ranking_average_precision_score(...)
Compute ranking-based average precision.
metrics.label_ranking_loss(y_true, y_score, *)
Compute Ranking loss measure.
### Clustering metrics
metrics.adjusted_mutual_info_score(...[, ...])
Adjusted Mutual Information between two clusterings.
metrics.adjusted_rand_score(labels_true, ...)
Rand index adjusted for chance.
metrics.calinski_harabasz_score(X, labels)
Compute the Calinski and Harabasz score.
metrics.davies_bouldin_score(X, labels)
Compute the Davies-Bouldin score.
metrics.completeness_score(labels_true, ...)
Compute completeness metric of a cluster labeling given a ground truth.
metrics.cluster.contingency_matrix(...[, ...])
Build a contingency matrix describing the relationship between labels.
metrics.cluster.pair_confusion_matrix(...)
Pair confusion matrix arising from two clusterings [R9ca8fd06d29a-1].
metrics.fowlkes_mallows_score(labels_true, ...)
Measure the similarity of two clusterings of a set of points.
metrics.homogeneity_completeness_v_measure(...)
Compute the homogeneity and completeness and V-Measure scores at once.
metrics.homogeneity_score(labels_true, ...)
Homogeneity metric of a cluster labeling given a ground truth.
metrics.mutual_info_score(labels_true, ...)
Mutual Information between two clusterings.
metrics.normalized_mutual_info_score(...[, ...])
Normalized Mutual Information between two clusterings.
metrics.rand_score(labels_true, labels_pred)
Rand index.
metrics.silhouette_score(X, labels, *[, ...])
Compute the mean Silhouette Coefficient of all samples.
metrics.silhouette_samples(X, labels, *[, ...])
Compute the Silhouette Coefficient for each sample.
metrics.v_measure_score(labels_true, ...[, beta])
V-measure cluster labeling given a ground truth.
### Biclustering metrics
metrics.consensus_score(a, b, *[, similarity])
The similarity of two sets of biclusters.
### Distance metrics
metrics.DistanceMetric
Uniform interface for fast distance metric functions.
### Pairwise metrics
metrics.pairwise.additive_chi2_kernel(X[, Y])
Compute the additive chi-squared kernel between observations in X and Y.
metrics.pairwise.chi2_kernel(X[, Y, gamma])
Compute the exponential chi-squared kernel between X and Y.
metrics.pairwise.cosine_similarity(X[, Y, ...])
Compute cosine similarity between samples in X and Y.
metrics.pairwise.cosine_distances(X[, Y])
Compute cosine distance between samples in X and Y.
metrics.pairwise.distance_metrics()
Valid metrics for pairwise_distances.
metrics.pairwise.euclidean_distances(X[, Y, ...])
Compute the distance matrix between each pair from a vector array X and Y.
metrics.pairwise.haversine_distances(X[, Y])
Compute the Haversine distance between samples in X and Y.
metrics.pairwise.kernel_metrics()
Valid metrics for pairwise_kernels.
metrics.pairwise.laplacian_kernel(X[, Y, gamma])
Compute the laplacian kernel between X and Y.
metrics.pairwise.linear_kernel(X[, Y, ...])
Compute the linear kernel between X and Y.
metrics.pairwise.manhattan_distances(X[, Y, ...])
Compute the L1 distances between the vectors in X and Y.
metrics.pairwise.nan_euclidean_distances(X)
Calculate the euclidean distances in the presence of missing values.
metrics.pairwise.pairwise_kernels(X[, Y, ...])
Compute the kernel between arrays X and optional array Y.
metrics.pairwise.polynomial_kernel(X[, Y, ...])
Compute the polynomial kernel between X and Y.
metrics.pairwise.rbf_kernel(X[, Y, gamma])
Compute the rbf (gaussian) kernel between X and Y.
metrics.pairwise.sigmoid_kernel(X[, Y, ...])
Compute the sigmoid kernel between X and Y.
metrics.pairwise.paired_euclidean_distances(X, Y)
Compute the paired euclidean distances between X and Y.
metrics.pairwise.paired_manhattan_distances(X, Y)
Compute the paired L1 distances between X and Y.
metrics.pairwise.paired_cosine_distances(X, Y)
Compute the paired cosine distances between X and Y.
metrics.pairwise.paired_distances(X, Y, *[, ...])
Compute the paired distances between X and Y.
metrics.pairwise_distances(X[, Y, metric, ...])
Compute the distance matrix from a vector array X and optional Y.
metrics.pairwise_distances_argmin(X, Y, *[, ...])
Compute minimum distances between one point and a set of points.
metrics.pairwise_distances_argmin_min(X, Y, *)
Compute minimum distances between one point and a set of points.
metrics.pairwise_distances_chunked(X[, Y, ...])
Generate a distance matrix chunk by chunk with optional reduction.
### Plotting
metrics.ConfusionMatrixDisplay(...[, ...])
Confusion Matrix visualization.
metrics.DetCurveDisplay(*, fpr, fnr[, ...])
DET curve visualization.
metrics.PrecisionRecallDisplay(precision, ...)
Precision Recall visualization.
metrics.PredictionErrorDisplay(*, y_true, y_pred)
Visualization of the prediction error of a regression model.
metrics.RocCurveDisplay(*, fpr, tpr[, ...])
ROC Curve visualization.
calibration.CalibrationDisplay(prob_true, ...)
Calibration curve (also known as reliability diagram) visualization.
## [sklearn.mixture: Gaussian Mixture Models](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.mixture)
mixture.BayesianGaussianMixture(*[, ...])
Variational Bayesian estimation of a Gaussian mixture.
mixture.GaussianMixture([n_components, ...])
Gaussian Mixture.
## [sklearn.model_selection: Model Selection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection)
### Splitter Classes
model_selection.GroupKFold([n_splits])
K-fold iterator variant with non-overlapping groups.
model_selection.GroupShuffleSplit([...])
Shuffle-Group(s)-Out cross-validation iterator
model_selection.KFold([n_splits, shuffle, ...])
K-Folds cross-validator
model_selection.LeaveOneGroupOut()
Leave One Group Out cross-validator
model_selection.LeavePGroupsOut(n_groups)
Leave P Group(s) Out cross-validator
model_selection.LeaveOneOut()
Leave-One-Out cross-validator
model_selection.LeavePOut(p)
Leave-P-Out cross-validator
model_selection.PredefinedSplit(test_fold)
Predefined split cross-validator
model_selection.RepeatedKFold(*[, n_splits, ...])
Repeated K-Fold cross validator.
model_selection.RepeatedStratifiedKFold(*[, ...])
Repeated Stratified K-Fold cross validator.
model_selection.ShuffleSplit([n_splits, ...])
Random permutation cross-validator
model_selection.StratifiedKFold([n_splits, ...])
Stratified K-Folds cross-validator.
model_selection.StratifiedShuffleSplit([...])
Stratified ShuffleSplit cross-validator
model_selection.StratifiedGroupKFold([...])
Stratified K-Folds iterator variant with non-overlapping groups.
model_selection.TimeSeriesSplit([n_splits, ...])
Time Series cross-validator
### Splitter Functions
model_selection.check_cv([cv, y, classifier])
Input checker utility for building a cross-validator.
model_selection.train_test_split(*arrays[, ...])
Split arrays or matrices into random train and test subsets.
##ä Hyper-parameter optimizers
model_selection.GridSearchCV(estimator, ...)
Exhaustive search over specified parameter values for an estimator.
model_selection.HalvingGridSearchCV(...[, ...])
Search over specified parameter values with successive halving.
model_selection.ParameterGrid(param_grid)
Grid of parameters with a discrete number of values for each.
model_selection.ParameterSampler(...[, ...])
Generator on parameters sampled from given distributions.
model_selection.RandomizedSearchCV(...[, ...])
Randomized search on hyper parameters.
model_selection.HalvingRandomSearchCV(...[, ...])
Randomized search on hyper parameters.
### Model validation
model_selection.cross_validate(estimator, X)
Evaluate metric(s) by cross-validation and also record fit/score times.
model_selection.cross_val_predict(estimator, X)
Generate cross-validated estimates for each input data point.
model_selection.cross_val_score(estimator, X)
Evaluate a score by cross-validation.
model_selection.learning_curve(estimator, X, ...)
Learning curve.
model_selection.permutation_test_score(...)
Evaluate the significance of a cross-validated score with permutations.
model_selection.validation_curve(estimator, ...)
Validation curve.
### Visualization
model_selection.LearningCurveDisplay(*, ...)
Learning Curve visualization.
model_selection.ValidationCurveDisplay(*, ...)
Validation Curve visualization.
## [sklearn.multiclass: Multiclass classification](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.multiclass)
multiclass.OneVsRestClassifier(estimator, *)
One-vs-the-rest (OvR) multiclass strategy.
multiclass.OneVsOneClassifier(estimator, *)
One-vs-one multiclass strategy.
multiclass.OutputCodeClassifier(estimator, *)
(Error-Correcting) Output-Code multiclass strategy.
## [sklearn.multioutput: Multioutput regression and classification](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.multioutput)
multioutput.ClassifierChain(base_estimator, *)
A multi-label model that arranges binary classifiers into a chain.
multioutput.MultiOutputRegressor(estimator, *)
Multi target regression.
multioutput.MultiOutputClassifier(estimator, *)
Multi target classification.
multioutput.RegressorChain(base_estimator, *)
A multi-label model that arranges regressions into a chain.
## [sklearn.naive_bayes: Naive Bayes](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.naive_bayes)
naive_bayes.BernoulliNB(*[, alpha, ...])
Naive Bayes classifier for multivariate Bernoulli models.
naive_bayes.CategoricalNB(*[, alpha, ...])
Naive Bayes classifier for categorical features.
naive_bayes.ComplementNB(*[, alpha, ...])
The Complement Naive Bayes classifier described in Rennie et al. (2003).
naive_bayes.GaussianNB(*[, priors, ...])
Gaussian Naive Bayes (GaussianNB).
naive_bayes.MultinomialNB(*[, alpha, ...])
Naive Bayes classifier for multinomial models.
## [sklearn.neighbors: Nearest Neighbors](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors)
neighbors.BallTree(X[, leaf_size, metric])
BallTree for fast generalized N-point problems
neighbors.KDTree(X[, leaf_size, metric])
KDTree for fast generalized N-point problems
neighbors.KernelDensity(*[, bandwidth, ...])
Kernel Density Estimation.
**neighbors.KNeighborsClassifier([...])**
**Classifier implementing the k-nearest neighbors vote.**
neighbors.KNeighborsRegressor([n_neighbors, ...])
Regression based on k-nearest neighbors.
neighbors.KNeighborsTransformer(*[, mode, ...])
Transform X into a (weighted) graph of k nearest neighbors.
neighbors.LocalOutlierFactor([n_neighbors, ...])
Unsupervised Outlier Detection using the Local Outlier Factor (LOF).
neighbors.RadiusNeighborsClassifier([...])
Classifier implementing a vote among neighbors within a given radius.
neighbors.RadiusNeighborsRegressor([radius, ...])
Regression based on neighbors within a fixed radius.
neighbors.RadiusNeighborsTransformer(*[, ...])
Transform X into a (weighted) graph of neighbors nearer than a radius.
neighbors.NearestCentroid([metric, ...])
Nearest centroid classifier.
neighbors.NearestNeighbors(*[, n_neighbors, ...])
Unsupervised learner for implementing neighbor searches.
neighbors.NeighborhoodComponentsAnalysis([...])
Neighborhood Components Analysis.
neighbors.kneighbors_graph(X, n_neighbors, *)
Compute the (weighted) graph of k-Neighbors for points in X.
neighbors.radius_neighbors_graph(X, radius, *)
Compute the (weighted) graph of Neighbors for points in X.
neighbors.sort_graph_by_row_values(graph[, ...])
Sort a sparse graph such that each row is stored with increasing values.
## [sklearn.neural_network: Neural network models](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neural_network)
pipeline.FeatureUnion(transformer_list, *[, ...])
Concatenates results of multiple transformer objects.
pipeline.Pipeline(steps, *[, memory, verbose])
Pipeline of transforms with a final estimator.
pipeline.make_pipeline(*steps[, memory, verbose])
Construct a Pipeline from the given estimators.
pipeline.make_union(*transformers[, n_jobs, ...])
Construct a FeatureUnion from the given transformers.
## [sklearn.pipeline: Pipeline](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.pipeline)
see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.pipeline)
## [sklearn.preprocessing: Preprocessing and Normalization](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing)