Context manager to temporarily change the global scikit-learn configuration.
Retrieve the current scikit-learn configuration.
Set global scikit-learn configuration.
Print useful debugging information”
Base class for all estimators in scikit-learn.
Mixin class for all bicluster estimators in scikit-learn.
ClassNamePrefixFeaturesOutMixin
Mixin class for transformers that generate their own names by prefixing.
Mixin class for all classifiers in scikit-learn.
Mixin class for all cluster estimators in scikit-learn.
Mixin class for all density estimators in scikit-learn.
Mixin class for all meta estimators in scikit-learn.
Provides get_feature_names_out for simple transformers.
Mixin class for all outlier detection estimators in scikit-learn.
Mixin class for all regression estimators in scikit-learn.
Mixin class for all transformers in scikit-learn.
Construct a new unfitted estimator with the same parameters.
Return True if the given estimator is (probably) a classifier.
Return True if the given estimator is (probably) a clusterer.
Return True if the given estimator is (probably) a regressor.
Return True if the given estimator is (probably) an outlier detector.
Probability calibration with isotonic regression or logistic regression.
Compute true and predicted probabilities for a calibration curve.
Calibration curve (also known as reliability diagram) visualization.
Perform Affinity Propagation Clustering of data.
Agglomerative Clustering.
Implements the BIRCH clustering algorithm.
Bisecting K-Means clustering.
Perform DBSCAN clustering from vector array or distance matrix.
Agglomerate features.
Cluster data using hierarchical density-based clustering.
K-Means clustering.
Mean shift clustering using a flat kernel.
Mini-Batch K-Means clustering.
Estimate clustering structure from vector array.
Spectral biclustering (Kluger, 2003).
Apply clustering to a projection of the normalized Laplacian.
Spectral Co-Clustering algorithm (Dhillon, 2001).
Perform Affinity Propagation Clustering of data.
Perform DBSCAN extraction for an arbitrary epsilon.
Automatically extract clusters according to the Xi-steep method.
Compute the OPTICS reachability graph.
Perform DBSCAN clustering from vector array or distance matrix.
Estimate the bandwidth to use with the mean-shift algorithm.
Perform K-means clustering algorithm.
Init n_clusters seeds according to k-means++.
Perform mean shift clustering of data using a flat kernel.
Apply clustering to a projection of the normalized Laplacian.
Ward clustering based on a Feature matrix.
Applies transformers to columns of an array or pandas DataFrame.
Meta-estimator to regress on a transformed target.
Create a callable to select columns to be used with
Construct a ColumnTransformer from the given transformers.
An object for detecting outliers in a Gaussian distributed dataset.
Maximum likelihood covariance estimator.
Sparse inverse covariance estimation with an l1-penalized estimator.
Sparse inverse covariance w/ cross-validated choice of the l1 penalty.
LedoitWolf Estimator.
Minimum Covariance Determinant (MCD): robust estimator of covariance.
Oracle Approximating Shrinkage Estimator.
Covariance estimator with shrinkage.
Compute the Maximum likelihood covariance estimator.
L1-penalized covariance estimator.
Estimate the shrunk Ledoit-Wolf covariance matrix.
Estimate the shrunk Ledoit-Wolf covariance matrix.
Estimate covariance with the Oracle Approximating Shrinkage.
Calculate covariance matrices shrunk on the diagonal.
Canonical Correlation Analysis, also known as “Mode B” PLS.
Partial Least Squares transformer and regressor.
PLS regression.
Partial Least Square SVD.
Delete all the content of the data home cache.
Dump the dataset in svmlight / libsvm file format.
Load the filenames and data from the 20 newsgroups dataset (classification).
Load and vectorize the 20 newsgroups dataset (classification).
Load the California housing dataset (regression).
Load the covertype dataset (classification).
Fetch a file from the web if not already present in the local folder.
Load the kddcup99 dataset (classification).
Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).
Load the Labeled Faces in the Wild (LFW) people dataset (classification).
Load the Olivetti faces data-set from AT&T (classification).
Fetch dataset from openml by name or dataset id.
Load the RCV1 multilabel dataset (classification).
Loader for species distribution dataset from Phillips et. al. (2006).
Return the path of the scikit-learn data directory.
Load and return the breast cancer Wisconsin dataset (classification).
Load and return the diabetes dataset (regression).
Load and return the digits dataset (classification).
Load text files with categories as subfolder names.
Load and return the iris dataset (classification).
Load and return the physical exercise Linnerud dataset.
Load the numpy array of a single sample image.
Load sample images for image manipulation.
Load datasets in the svmlight / libsvm format into sparse CSR matrix.
Load dataset from multiple files in SVMlight format.
Load and return the wine dataset (classification).
Generate a constant block diagonal structure array for biclustering.
Generate isotropic Gaussian blobs for clustering.
Generate an array with block checkerboard structure for biclustering.
Make a large circle containing a smaller circle in 2d.
Generate a random n-class classification problem.
Generate the “Friedman #1” regression problem.
Generate the “Friedman #2” regression problem.
Generate the “Friedman #3” regression problem.
Generate isotropic Gaussian and label samples by quantile.
Generate data for binary classification used in Hastie et al. 2009, Example 10.2.
Generate a mostly low rank matrix with bell-shaped singular values.
Make two interleaving half circles.
make_multilabel_classification
Generate a random multilabel classification problem.
Generate a random regression problem.
Generate an S curve dataset.
Generate a signal as a sparse combination of dictionary elements.
Generate a sparse symmetric definite positive matrix.
Generate a random regression problem with sparse uncorrelated design.
Generate a random symmetric, positive-definite matrix.
Generate a swiss roll dataset.
Dictionary learning.
Factor Analysis (FA).
FastICA: a fast algorithm for Independent Component Analysis.
Incremental principal components analysis (IPCA).
Kernel Principal component analysis (KPCA).
Latent Dirichlet Allocation with online variational Bayes algorithm.
Mini-batch dictionary learning.
Mini-Batch Non-Negative Matrix Factorization (NMF).
Mini-batch Sparse Principal Components Analysis.
Non-Negative Matrix Factorization (NMF).
Principal component analysis (PCA).
Sparse coding.
Sparse Principal Components Analysis (SparsePCA).
Dimensionality reduction using truncated SVD (aka LSA).
Solve a dictionary learning matrix factorization problem.
Solve a dictionary learning matrix factorization problem online.
Perform Fast Independent Component Analysis.
Compute Non-negative Matrix Factorization (NMF).
Sparse coding.
Linear Discriminant Analysis.
Quadratic Discriminant Analysis.
DummyClassifier makes predictions that ignore the input features.
Regressor that makes predictions using simple rules.
An AdaBoost classifier.
An AdaBoost regressor.
A Bagging classifier.
A Bagging regressor.
An extra-trees classifier.
An extra-trees regressor.
Gradient Boosting for classification.
Gradient Boosting for regression.
HistGradientBoostingClassifier
Histogram-based Gradient Boosting Classification Tree.
Histogram-based Gradient Boosting Regression Tree.
Isolation Forest Algorithm.
A random forest classifier.
A random forest regressor.
An ensemble of totally random trees.
Stack of estimators with a final classifier.
Stack of estimators with a final regressor.
Soft Voting/Majority Rule classifier for unfitted estimators.
Prediction voting regressor for unfitted estimators.
Custom warning to capture convergence problems
Warning used to notify implicit data conversions happening in the code.
Custom warning to notify potential issues with data dimensionality.
Warning used to notify the user of inefficient computation.
Warning class used if there is an error while fitting the estimator.
Warning raised when an estimator is unpickled with an inconsistent version.
Exception class to raise if estimator is used before fitting.
Warning used when the metric is invalid
Warning raised when an estimator check from the common tests fails.
Enables Successive Halving search-estimators
Enables IterativeImputer
Transforms lists of feature-value mappings to vectors.
Implements feature hashing, aka the hashing trick.
Extracts patches from a collection of images.
sklearn.feature_extraction.image
Reshape a 2D image into a collection of patches.
sklearn.feature_extraction.image
Graph of the pixel-to-pixel connections.
sklearn.feature_extraction.image
Graph of the pixel-to-pixel gradient connections.
sklearn.feature_extraction.image
Reconstruct the image from all of its patches.
sklearn.feature_extraction.image
Convert a collection of text documents to a matrix of token counts.
sklearn.feature_extraction.text
Convert a collection of text documents to a matrix of token occurrences.
sklearn.feature_extraction.text
Transform a count matrix to a normalized tf or tf-idf representation.
sklearn.feature_extraction.text
Convert a collection of raw documents to a matrix of TF-IDF features.
sklearn.feature_extraction.text
Univariate feature selector with configurable strategy.
Feature ranking with recursive feature elimination.
Recursive feature elimination with cross-validation to select features.
Filter: Select the p-values for an estimated false discovery rate.
Filter: Select the pvalues below alpha based on a FPR test.
Meta-transformer for selecting features based on importance weights.
Filter: Select the p-values corresponding to Family-wise error rate.
Select features according to the k highest scores.
Select features according to a percentile of the highest scores.
Transformer mixin that performs feature selection given a support mask
Transformer that performs Sequential Feature Selection.
Feature selector that removes all low-variance features.
Compute chi-squared stats between each non-negative feature and class.
Compute the ANOVA F-value for the provided sample.
Univariate linear regression tests returning F-statistic and p-values.
Estimate mutual information for a discrete target variable.
Estimate mutual information for a continuous target variable.
Compute Pearson’s r for each features and the target.
Estimator that wraps a fitted estimator to prevent re-fitting.
Gaussian process classification (GPC) based on Laplace approximation.
Gaussian process regression (GPR).
Kernel which is composed of a set of other kernels.
sklearn.gaussian_process.kernels
Constant kernel.
sklearn.gaussian_process.kernels
Dot-Product kernel.
sklearn.gaussian_process.kernels
Exp-Sine-Squared kernel (aka periodic kernel).
sklearn.gaussian_process.kernels
The Exponentiation kernel takes one base kernel and a scalar parameter
sklearn.gaussian_process.kernels
A kernel hyperparameter’s specification in form of a namedtuple.
sklearn.gaussian_process.kernels
Base class for all kernels.
sklearn.gaussian_process.kernels
Matern kernel.
sklearn.gaussian_process.kernels
Wrapper for kernels in sklearn.metrics.pairwise.
sklearn.gaussian_process.kernels
The Product kernel takes two kernels \(k_1\) and \(k_2\)
sklearn.gaussian_process.kernels
Radial basis function kernel (aka squared-exponential kernel).
sklearn.gaussian_process.kernels
Rational Quadratic kernel.
sklearn.gaussian_process.kernels
The Sum kernel takes two kernels \(k_1\) and \(k_2\)
sklearn.gaussian_process.kernels
White kernel.
sklearn.gaussian_process.kernels
Multivariate imputer that estimates each feature from all the others.
Imputation for completing missing values using k-Nearest Neighbors.
Binary indicators for missing values.
Univariate imputer for completing missing values with simple strategies.
Partial dependence of features.
Permutation importance for feature evaluation [Rd9e56ef97513-BRE].
Decisions boundary visualization.
Partial Dependence Plot (PDP) and Individual Conditional Expectation (ICE).
Isotonic regression model.
Determine whether y is monotonically correlated with x.
Solve the isotonic regression model.
Approximate feature map for additive chi2 kernel.
Approximate a kernel map using a subset of the training data.
Polynomial kernel approximation via Tensor Sketch.
Approximate a RBF kernel feature map using random Fourier features.
Approximate feature map for “skewed chi-squared” kernel.
Kernel ridge regression.
Logistic Regression (aka logit, MaxEnt) classifier.
Logistic Regression CV (aka logit, MaxEnt) classifier.
Passive Aggressive Classifier.
Linear perceptron classifier.
Classifier using Ridge regression.
Ridge classifier with built-in cross-validation.
Linear classifiers (SVM, logistic regression, etc.) with SGD training.
Solves linear One-Class SVM using Stochastic Gradient Descent.
Ordinary least squares Linear Regression.
Linear least squares with l2 regularization.
Ridge regression with built-in cross-validation.
Linear model fitted by minimizing a regularized empirical loss with SGD.
Linear regression with combined L1 and L2 priors as regularizer.
Elastic Net model with iterative fitting along a regularization path.
Least Angle Regression model a.k.a. LAR.
Cross-validated Least Angle Regression model.
Linear Model trained with L1 prior as regularizer (aka the Lasso).
Lasso linear model with iterative fitting along a regularization path.
Lasso model fit with Least Angle Regression a.k.a. Lars.
Cross-validated Lasso, using the LARS algorithm.
Lasso model fit with Lars using BIC or AIC for model selection.
Orthogonal Matching Pursuit model (OMP).
Cross-validated Orthogonal Matching Pursuit model (OMP).
Bayesian ARD regression.
Bayesian ridge regression.
Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.
Multi-task L1/L2 ElasticNet with built-in cross-validation.
Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.
Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.
L2-regularized linear regression model that is robust to outliers.
Linear regression model that predicts conditional quantiles.
RANSAC (RANdom SAmple Consensus) algorithm.
Theil-Sen Estimator: robust multivariate regression model.
Generalized Linear Model with a Gamma distribution.
Generalized Linear Model with a Poisson distribution.
Generalized Linear Model with a Tweedie distribution.
Passive Aggressive Regressor.
Compute elastic net path with coordinate descent.
Compute Least Angle Regression or Lasso path using the LARS algorithm.
The lars_path in the sufficient stats mode.
Compute Lasso path with coordinate descent.
Orthogonal Matching Pursuit (OMP).
Gram Orthogonal Matching Pursuit (OMP).
Solve the ridge equation by the method of normal equations.
Isomap Embedding.
Locally Linear Embedding.
Multidimensional scaling.
Spectral embedding for non-linear dimensionality reduction.
T-distributed Stochastic Neighbor Embedding.
Perform a Locally Linear Embedding analysis on the data.
Compute multidimensional scaling using the SMACOF algorithm.
Project the sample on the first eigenvectors of the graph Laplacian.
Indicate to what extent the local structure is retained.
Determine scorer from user options.
Get a scorer from string.
Get the names of all available scorers.
Make a scorer from a performance metric or loss function.
Accuracy classification score.
Compute Area Under the Curve (AUC) using the trapezoidal rule.
Compute average precision (AP) from prediction scores.
Compute the balanced accuracy.
Compute the Brier score loss.
Compute binary classification positive and negative likelihood ratios.
Build a text report showing the main classification metrics.
Compute Cohen’s kappa: a statistic that measures inter-annotator agreement.
Compute confusion matrix to evaluate the accuracy of a classification.
\(D^2\) score function, fraction of log loss explained.
Compute Discounted Cumulative Gain.
Compute Detection Error Tradeoff (DET) for different probability thresholds.
Compute the F1 score, also known as balanced F-score or F-measure.
Compute the F-beta score.
Compute the average Hamming loss.
Average hinge loss (non-regularized).
Jaccard similarity coefficient score.
Log loss, aka logistic loss or cross-entropy loss.
Compute the Matthews correlation coefficient (MCC).
Compute a confusion matrix for each class or sample.
Compute Normalized Discounted Cumulative Gain.
Compute precision-recall pairs for different probability thresholds.
precision_recall_fscore_support
Compute precision, recall, F-measure and support for each class.
Compute the precision.
Compute the recall.
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
Compute Receiver operating characteristic (ROC).
Top-k Accuracy classification score.
Zero-one classification loss.
\(D^2\) regression score function, fraction of absolute error explained.
\(D^2\) regression score function, fraction of pinball loss explained.
\(D^2\) regression score function, fraction of Tweedie deviance explained.
Explained variance regression score function.
The max_error metric calculates the maximum residual error.
Mean absolute error regression loss.
mean_absolute_percentage_error
Mean absolute percentage error (MAPE) regression loss.
Mean Gamma deviance regression loss.
Pinball loss for quantile regression.
Mean Poisson deviance regression loss.
Mean squared error regression loss.
Mean squared logarithmic error regression loss.
Mean Tweedie deviance regression loss.
Median absolute error regression loss.
\(R^2\) (coefficient of determination) regression score function.
Root mean squared error regression loss.
Root mean squared logarithmic error regression loss.
Coverage error measure.
label_ranking_average_precision_score
Compute ranking-based average precision.
Compute Ranking loss measure.
Adjusted Mutual Information between two clusterings.
Rand index adjusted for chance.
Compute the Calinski and Harabasz score.
Build a contingency matrix describing the relationship between labels.
Pair confusion matrix arising from two clusterings.
Compute completeness metric of a cluster labeling given a ground truth.
Compute the Davies-Bouldin score.
Measure the similarity of two clusterings of a set of points.
homogeneity_completeness_v_measure
Compute the homogeneity and completeness and V-Measure scores at once.
Homogeneity metric of a cluster labeling given a ground truth.
Mutual Information between two clusterings.
Normalized Mutual Information between two clusterings.
Rand index.
Compute the Silhouette Coefficient for each sample.
Compute the mean Silhouette Coefficient of all samples.
V-measure cluster labeling given a ground truth.
The similarity of two sets of biclusters.
Uniform interface for fast distance metric functions.
Compute the additive chi-squared kernel between observations in X and Y.
Compute the exponential chi-squared kernel between X and Y.
Compute cosine distance between samples in X and Y.
Compute cosine similarity between samples in X and Y.
Valid metrics for pairwise_distances.
Compute the distance matrix between each pair from a feature array X and Y.
Compute the Haversine distance between samples in X and Y.
Valid metrics for pairwise_kernels.
Compute the laplacian kernel between X and Y.
Compute the linear kernel between X and Y.
Compute the L1 distances between the vectors in X and Y.
Calculate the euclidean distances in the presence of missing values.
Compute the paired cosine distances between X and Y.
Compute the paired distances between X and Y.
Compute the paired euclidean distances between X and Y.
Compute the paired L1 distances between X and Y.
Compute the kernel between arrays X and optional array Y.
Compute the polynomial kernel between X and Y.
Compute the rbf (gaussian) kernel between X and Y.
Compute the sigmoid kernel between X and Y.
Compute the distance matrix from a feature array X and optional Y.
Compute minimum distances between one point and a set of points.
Compute minimum distances between one point and a set of points.
Generate a distance matrix chunk by chunk with optional reduction.
Confusion Matrix visualization.
Detection Error Tradeoff (DET) curve visualization.
Precision Recall visualization.
Visualization of the prediction error of a regression model.
ROC Curve visualization.
Variational Bayesian estimation of a Gaussian mixture.
Gaussian Mixture.
K-fold iterator variant with non-overlapping groups.
Shuffle-Group(s)-Out cross-validation iterator.
K-Fold cross-validator.
Leave One Group Out cross-validator.
Leave-One-Out cross-validator.
Leave P Group(s) Out cross-validator.
Leave-P-Out cross-validator.
Predefined split cross-validator.
Repeated K-Fold cross validator.
Repeated class-wise stratified K-Fold cross validator.
Random permutation cross-validator.
Class-wise stratified K-Fold iterator variant with non-overlapping groups.
Class-wise stratified K-Fold cross-validator.
Class-wise stratified ShuffleSplit cross-validator.
Time Series cross-validator.
Input checker utility for building a cross-validator.
Split arrays or matrices into random train and test subsets.
Exhaustive search over specified parameter values for an estimator.
Search over specified parameter values with successive halving.
Randomized search on hyper parameters.
Grid of parameters with a discrete number of values for each.
Generator on parameters sampled from given distributions.
Randomized search on hyper parameters.
Binary classifier that manually sets the decision threshold.
Classifier that post-tunes the decision threshold using cross-validation.
Generate cross-validated estimates for each input data point.
Evaluate a score by cross-validation.
Evaluate metric(s) by cross-validation and also record fit/score times.
Learning curve.
Evaluate the significance of a cross-validated score with permutations.
Validation curve.
Learning Curve visualization.
Validation Curve visualization.
One-vs-one multiclass strategy.
One-vs-the-rest (OvR) multiclass strategy.
(Error-Correcting) Output-Code multiclass strategy.
A multi-label model that arranges binary classifiers into a chain.
Multi target classification.
Multi target regression.
A multi-label model that arranges regressions into a chain.
Naive Bayes classifier for multivariate Bernoulli models.
Naive Bayes classifier for categorical features.
The Complement Naive Bayes classifier described in Rennie et al. (2003).
Gaussian Naive Bayes (GaussianNB).
Naive Bayes classifier for multinomial models.
BallTree for fast generalized N-point problems
KDTree for fast generalized N-point problems
Classifier implementing the k-nearest neighbors vote.
Regression based on k-nearest neighbors.
Transform X into a (weighted) graph of k nearest neighbors.
Kernel Density Estimation.
Unsupervised Outlier Detection using the Local Outlier Factor (LOF).
Nearest centroid classifier.
Unsupervised learner for implementing neighbor searches.
NeighborhoodComponentsAnalysis
Neighborhood Components Analysis.
Classifier implementing a vote among neighbors within a given radius.
Regression based on neighbors within a fixed radius.
Transform X into a (weighted) graph of neighbors nearer than a radius.
Compute the (weighted) graph of k-Neighbors for points in X.
Compute the (weighted) graph of Neighbors for points in X.
Sort a sparse graph such that each row is stored with increasing values.
Bernoulli Restricted Boltzmann Machine (RBM).
Multi-layer Perceptron classifier.
Multi-layer Perceptron regressor.
Concatenates results of multiple transformer objects.
A sequence of data transformers with an optional final predictor.
Construct a Pipeline from the given estimators.
Construct a FeatureUnion from the given transformers.
Binarize data (set feature values to 0 or 1) according to a threshold.
Constructs a transformer from an arbitrary callable.
Bin continuous data into intervals.
Center an arbitrary kernel matrix \(K\).
Binarize labels in a one-vs-all fashion.
Encode target labels with value between 0 and n_classes-1.
Scale each feature by its maximum absolute value.
Transform features by scaling each feature to a given range.
Transform between iterable of iterables and a multilabel format.
Normalize samples individually to unit norm.
Encode categorical features as a one-hot numeric array.
Encode categorical features as an integer array.
Generate polynomial and interaction features.
Apply a power transform featurewise to make data more Gaussian-like.
Transform features using quantiles information.
Scale features using statistics that are robust to outliers.
Generate univariate B-spline bases for features.
Standardize features by removing the mean and scaling to unit variance.
Target Encoder for regression and classification targets.
Augment dataset with an additional dummy feature.
Boolean thresholding of array-like or scipy.sparse matrix.
Binarize labels in a one-vs-all fashion.
Scale each feature to the [-1, 1] range without breaking the sparsity.
Transform features by scaling each feature to a given range.
Scale input vectors individually to unit norm (vector length).
Parametric, monotonic transformation to make data more Gaussian-like.
Transform features using quantiles information.
Standardize a dataset along any axis.
Standardize a dataset along any axis.
Reduce dimensionality through Gaussian random projection.
Reduce dimensionality through sparse random projection.
Find a ‘safe’ number of components to randomly project to.
Label Propagation classifier.
LabelSpreading model for semi-supervised learning.
Self-training classifier.
Linear Support Vector Classification.
Linear Support Vector Regression.
Nu-Support Vector Classification.
Nu Support Vector Regression.
Unsupervised Outlier Detection.
C-Support Vector Classification.
Epsilon-Support Vector Regression.
Return the lowest bound for C.
A decision tree classifier.
A decision tree regressor.
An extremely randomized tree classifier.
An extremely randomized tree regressor.
Export a decision tree in DOT format.
Build a text report showing the rules of a decision tree.
Plot a decision tree.
Container object exposing keys as attributes.
Return rows, items or columns of X using indices.
Convert an array-like to an array of floats.
Throw a ValueError if X contains NaN or infinity.
Decorator to mark a function or class as deprecated.
Build a HTML representation of an estimator.
Generator to create slices containing batch_size elements from 0 to n.
Generator to create n_packs evenly spaced slices going up to n.
Make arrays indexable for cross-validation.
Compute the 32bit murmurhash3 of key at seed.
Resample arrays or sparse matrices in a consistent way.
Return a mask which is safe to use on X.
Element wise squaring of array-likes and sparse matrices.
Shuffle arrays or sparse matrices in a consistent way.
Tags for the estimator.
Tags for the input data.
Tags for the target data.
Tags for the classifier.
Tags for the regressor.
Tags for the transformer.
Get estimator tags.
Input validation for standard estimators.
Input validation on an array, list, sparse matrix or similar.
Check that all arrays have consistent first dimensions.
Turn seed into a np.random.RandomState instance.
Validate scalar parameters type and value.
Perform is_fitted validation for estimator.
Check that memory is joblib.Memory-like.
Make sure that array is 2D, square and symmetric.
Ravel column or 1d numpy array, else raises an error.
Check whether the estimator’s fit method supports the given parameter.
Validate input data and set or check feature names and counts of the input.
An attribute that is available only if check returns a truthy value.
Estimate class weights for unbalanced datasets.
Estimate sample weights by class for unbalanced datasets.
Check if y is in a multilabel format.
Determine the type of data indicated by the target.
Extract an ordered array of unique labels.
Compute density of a sparse vector.
Compute logarithm of determinant of a square matrix.
Compute an orthonormal matrix whose range approximates the range of A.
Compute a truncated randomized SVD.
Dot product that handle the sparse matrix case correctly.
Return an array of the weighted modal (most common) value in the passed array.
Compute incremental mean and variance along an axis on a CSR or CSC matrix.
Inplace column scaling of a CSC/CSR matrix.
Inplace column scaling of a CSR matrix.
Inplace row scaling of a CSR or CSC matrix.
Swap two columns of a CSC/CSR matrix in-place.
Swap two rows of a CSC/CSR matrix in-place.
Compute mean and variance along an axis on a CSR or CSC matrix.
Normalize inplace the rows of a CSR matrix or array by their L1 norm.
sklearn.utils.sparsefuncs_fast
Normalize inplace the rows of a CSR matrix or array by their L2 norm.
sklearn.utils.sparsefuncs_fast
single_source_shortest_path_length
Return the length of the shortest path from source to all reachable nodes.
Sample integers without replacement.
Find the minimum value of an array over positive values.
Contains the metadata request info of a consumer.
sklearn.utils.metadata_routing
Coordinates metadata routing for a router object.
sklearn.utils.metadata_routing
Stores the mapping between caller and callee methods for a router.
sklearn.utils.metadata_routing
Get a Metadata{Router, Request} instance from the given object.
sklearn.utils.metadata_routing
Validate and route metadata.
sklearn.utils.metadata_routing
Get a list of all displays from sklearn.
Get a list of all estimators from sklearn.
Get a list of all functions from sklearn.
Check if estimator adheres to scikit-learn conventions.
sklearn.utils.estimator_checks
Pytest specific decorator for parametrizing estimator checks.
sklearn.utils.estimator_checks
Iteratively yield all check callables for an estimator.
sklearn.utils.estimator_checks
Tweak of joblib.Parallel that propagates the scikit-learn configuration.
Decorator used to capture the arguments of a function.
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