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Examples¶

Release Highlights¶

These examples illustrate the main features of the releases of scikit-learn.

Release Highlights for scikit-learn 0.23 — Release Highlights for scikit-learn 0.23¶

Release Highlights for scikit-learn 0.24 — Release Highlights for scikit-learn 0.24¶

Release Highlights for scikit-learn 0.22 — Release Highlights for scikit-learn 0.22¶

Biclustering¶

Examples concerning the sklearn.cluster.bicluster module.

A demo of the Spectral Co-Clustering algorithm — A demo of the Spectral Co-Clustering algorithm¶

A demo of the Spectral Biclustering algorithm — A demo of the Spectral Biclustering algorithm¶

Biclustering documents with the Spectral Co-clustering algorithm — Biclustering documents with the Spectral Co-clustering algorithm¶

Calibration¶

Examples illustrating the calibration of predicted probabilities of classifiers.

Comparison of Calibration of Classifiers — Comparison of Calibration of Classifiers¶

Probability Calibration curves — Probability Calibration curves¶

Probability calibration of classifiers — Probability calibration of classifiers¶

Probability Calibration for 3-class classification — Probability Calibration for 3-class classification¶

Classification¶

General examples about classification algorithms.

Recognizing hand-written digits — Recognizing hand-written digits¶

Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification — Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification¶

Plot classification probability — Plot classification probability¶

Classifier comparison — Classifier comparison¶

Linear and Quadratic Discriminant Analysis with covariance ellipsoid — Linear and Quadratic Discriminant Analysis with covariance ellipsoid¶

Clustering¶

Examples concerning the sklearn.cluster module.

An example of K-Means++ initialization — An example of K-Means++ initialization¶

Plot Hierarchical Clustering Dendrogram — Plot Hierarchical Clustering Dendrogram¶

Feature agglomeration — Feature agglomeration¶

A demo of the mean-shift clustering algorithm — A demo of the mean-shift clustering algorithm¶

Demonstration of k-means assumptions — Demonstration of k-means assumptions¶

Online learning of a dictionary of parts of faces — Online learning of a dictionary of parts of faces¶

Vector Quantization Example — Vector Quantization Example¶

Demo of affinity propagation clustering algorithm — Demo of affinity propagation clustering algorithm¶

Agglomerative clustering with and without structure — Agglomerative clustering with and without structure¶

Various Agglomerative Clustering on a 2D embedding of digits — Various Agglomerative Clustering on a 2D embedding of digits¶

Segmenting the picture of greek coins in regions — Segmenting the picture of greek coins in regions¶

K-means Clustering — K-means Clustering¶

Spectral clustering for image segmentation — Spectral clustering for image segmentation¶

A demo of structured Ward hierarchical clustering on an image of coins — A demo of structured Ward hierarchical clustering on an image of coins¶

Demo of DBSCAN clustering algorithm — Demo of DBSCAN clustering algorithm¶

Color Quantization using K-Means — Color Quantization using K-Means¶

Hierarchical clustering: structured vs unstructured ward — Hierarchical clustering: structured vs unstructured ward¶

Agglomerative clustering with different metrics — Agglomerative clustering with different metrics¶

Inductive Clustering — Inductive Clustering¶

Demo of OPTICS clustering algorithm — Demo of OPTICS clustering algorithm¶

Compare BIRCH and MiniBatchKMeans — Compare BIRCH and MiniBatchKMeans¶

Empirical evaluation of the impact of k-means initialization — Empirical evaluation of the impact of k-means initialization¶

Adjustment for chance in clustering performance evaluation — Adjustment for chance in clustering performance evaluation¶

Comparison of the K-Means and MiniBatchKMeans clustering algorithms — Comparison of the K-Means and MiniBatchKMeans clustering algorithms¶

Feature agglomeration vs. univariate selection — Feature agglomeration vs. univariate selection¶

Comparing different hierarchical linkage methods on toy datasets — Comparing different hierarchical linkage methods on toy datasets¶

A demo of K-Means clustering on the handwritten digits data — A demo of K-Means clustering on the handwritten digits data¶

Selecting the number of clusters with silhouette analysis on KMeans clustering — Selecting the number of clusters with silhouette analysis on KMeans clustering¶

Comparing different clustering algorithms on toy datasets — Comparing different clustering algorithms on toy datasets¶

Covariance estimation¶

Examples concerning the sklearn.covariance module.

Ledoit-Wolf vs OAS estimation — Ledoit-Wolf vs OAS estimation¶

Sparse inverse covariance estimation — Sparse inverse covariance estimation¶

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood — Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood¶

Robust covariance estimation and Mahalanobis distances relevance — Robust covariance estimation and Mahalanobis distances relevance¶

Robust vs Empirical covariance estimate — Robust vs Empirical covariance estimate¶

Cross decomposition¶

Examples concerning the sklearn.cross_decomposition module.

Principal Component Regression vs Partial Least Squares Regression — Principal Component Regression vs Partial Least Squares Regression¶

Compare cross decomposition methods — Compare cross decomposition methods¶

Dataset examples¶

Examples concerning the sklearn.datasets module.

The Digit Dataset — The Digit Dataset¶

The Iris Dataset — The Iris Dataset¶

Plot randomly generated classification dataset — Plot randomly generated classification dataset¶

Plot randomly generated multilabel dataset — Plot randomly generated multilabel dataset¶

Decision Trees¶

Examples concerning the sklearn.tree module.

Decision Tree Regression — Decision Tree Regression¶

Multi-output Decision Tree Regression — Multi-output Decision Tree Regression¶

Plot the decision surface of a decision tree on the iris dataset — Plot the decision surface of a decision tree on the iris dataset¶

Post pruning decision trees with cost complexity pruning — Post pruning decision trees with cost complexity pruning¶

Understanding the decision tree structure — Understanding the decision tree structure¶

Ensemble methods¶

Examples concerning the sklearn.ensemble module.

Pixel importances with a parallel forest of trees — Pixel importances with a parallel forest of trees¶

Decision Tree Regression with AdaBoost — Decision Tree Regression with AdaBoost¶

Plot individual and voting regression predictions — Plot individual and voting regression predictions¶

IsolationForest example — IsolationForest example¶

Monotonic Constraints — Monotonic Constraints¶

Plot the decision boundaries of a VotingClassifier — Plot the decision boundaries of a VotingClassifier¶

Feature importances with a forest of trees — Feature importances with a forest of trees¶

Comparing random forests and the multi-output meta estimator — Comparing random forests and the multi-output meta estimator¶

Prediction Intervals for Gradient Boosting Regression — Prediction Intervals for Gradient Boosting Regression¶

Gradient Boosting regularization — Gradient Boosting regularization¶

Plot class probabilities calculated by the VotingClassifier — Plot class probabilities calculated by the VotingClassifier¶

Gradient Boosting regression — Gradient Boosting regression¶

OOB Errors for Random Forests — OOB Errors for Random Forests¶

Two-class AdaBoost — Two-class AdaBoost¶

Hashing feature transformation using Totally Random Trees — Hashing feature transformation using Totally Random Trees¶

Multi-class AdaBoosted Decision Trees — Multi-class AdaBoosted Decision Trees¶

Discrete versus Real AdaBoost — Discrete versus Real AdaBoost¶

Feature transformations with ensembles of trees — Feature transformations with ensembles of trees¶

Early stopping of Gradient Boosting — Early stopping of Gradient Boosting¶

Gradient Boosting Out-of-Bag estimates — Gradient Boosting Out-of-Bag estimates¶

Single estimator versus bagging: bias-variance decomposition — Single estimator versus bagging: bias-variance decomposition¶

Categorical Feature Support in Gradient Boosting — Categorical Feature Support in Gradient Boosting¶

Plot the decision surfaces of ensembles of trees on the iris dataset — Plot the decision surfaces of ensembles of trees on the iris dataset¶

Combine predictors using stacking — Combine predictors using stacking¶

Examples based on real world datasets¶

Applications to real world problems with some medium sized datasets or interactive user interface.

Outlier detection on a real data set — Outlier detection on a real data set¶

Compressive sensing: tomography reconstruction with L1 prior (Lasso) — Compressive sensing: tomography reconstruction with L1 prior (Lasso)¶

Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation — Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation¶

Faces recognition example using eigenfaces and SVMs — Faces recognition example using eigenfaces and SVMs¶

Model Complexity Influence — Model Complexity Influence¶

Visualizing the stock market structure — Visualizing the stock market structure¶

Wikipedia principal eigenvector — Wikipedia principal eigenvector¶

Species distribution modeling — Species distribution modeling¶

Libsvm GUI — Libsvm GUI¶

Prediction Latency — Prediction Latency¶

Out-of-core classification of text documents — Out-of-core classification of text documents¶

Feature Selection¶

Examples concerning the sklearn.feature_selection module.

Recursive feature elimination — Recursive feature elimination¶

Comparison of F-test and mutual information — Comparison of F-test and mutual information¶

Pipeline ANOVA SVM — Pipeline ANOVA SVM¶

Recursive feature elimination with cross-validation — Recursive feature elimination with cross-validation¶

Model-based and sequential feature selection — Model-based and sequential feature selection¶

Test with permutations the significance of a classification score — Test with permutations the significance of a classification score¶

Univariate Feature Selection — Univariate Feature Selection¶

Gaussian Mixture Models¶

Examples concerning the sklearn.mixture module.

Density Estimation for a Gaussian mixture — Density Estimation for a Gaussian mixture¶

Gaussian Mixture Model Ellipsoids — Gaussian Mixture Model Ellipsoids¶

Gaussian Mixture Model Selection — Gaussian Mixture Model Selection¶

GMM covariances — GMM covariances¶

Gaussian Mixture Model Sine Curve — Gaussian Mixture Model Sine Curve¶

Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture — Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture¶

Generalized Linear Models¶

Examples concerning the sklearn.linear_model module.

Lasso path using LARS — Lasso path using LARS¶

Plot Ridge coefficients as a function of the regularization — Plot Ridge coefficients as a function of the regularization¶

SGD: Maximum margin separating hyperplane — SGD: Maximum margin separating hyperplane¶

SGD: convex loss functions — SGD: convex loss functions¶

Ordinary Least Squares and Ridge Regression Variance — Ordinary Least Squares and Ridge Regression Variance¶

Plot Ridge coefficients as a function of the L2 regularization — Plot Ridge coefficients as a function of the L2 regularization¶

SGD: Penalties — SGD: Penalties¶

Logistic function — Logistic function¶

Polynomial interpolation — Polynomial interpolation¶

Regularization path of L1- Logistic Regression — Regularization path of L1- Logistic Regression¶

Logistic Regression 3-class Classifier — Logistic Regression 3-class Classifier¶

SGD: Weighted samples — SGD: Weighted samples¶

Non-negative least squares — Non-negative least squares¶

Linear Regression Example — Linear Regression Example¶

Robust linear model estimation using RANSAC — Robust linear model estimation using RANSAC¶

Sparsity Example: Fitting only features 1 and 2 — Sparsity Example: Fitting only features 1 and 2¶

HuberRegressor vs Ridge on dataset with strong outliers — HuberRegressor vs Ridge on dataset with strong outliers¶

Lasso on dense and sparse data — Lasso on dense and sparse data¶

Comparing various online solvers — Comparing various online solvers¶

Joint feature selection with multi-task Lasso — Joint feature selection with multi-task Lasso¶

MNIST classification using multinomial logistic + L1 — MNIST classification using multinomial logistic + L1¶

Plot multi-class SGD on the iris dataset — Plot multi-class SGD on the iris dataset¶

Orthogonal Matching Pursuit — Orthogonal Matching Pursuit¶

Lasso and Elastic Net for Sparse Signals — Lasso and Elastic Net for Sparse Signals¶

Curve Fitting with Bayesian Ridge Regression — Curve Fitting with Bayesian Ridge Regression¶

Theil-Sen Regression — Theil-Sen Regression¶

Plot multinomial and One-vs-Rest Logistic Regression — Plot multinomial and One-vs-Rest Logistic Regression¶

Robust linear estimator fitting — Robust linear estimator fitting¶

L1 Penalty and Sparsity in Logistic Regression — L1 Penalty and Sparsity in Logistic Regression¶

Lasso and Elastic Net — Lasso and Elastic Net¶

Automatic Relevance Determination Regression (ARD) — Automatic Relevance Determination Regression (ARD)¶

Bayesian Ridge Regression — Bayesian Ridge Regression¶

Lasso model selection: Cross-Validation / AIC / BIC — Lasso model selection: Cross-Validation / AIC / BIC¶

Multiclass sparse logistic regression on 20newgroups — Multiclass sparse logistic regression on 20newgroups¶

Early stopping of Stochastic Gradient Descent — Early stopping of Stochastic Gradient Descent¶

Poisson regression and non-normal loss — Poisson regression and non-normal loss¶

Tweedie regression on insurance claims — Tweedie regression on insurance claims¶

Inspection¶

Examples related to the sklearn.inspection module.

Permutation Importance with Multicollinear or Correlated Features — Permutation Importance with Multicollinear or Correlated Features¶

Permutation Importance vs Random Forest Feature Importance (MDI) — Permutation Importance vs Random Forest Feature Importance (MDI)¶

Partial Dependence and Individual Conditional Expectation Plots — Partial Dependence and Individual Conditional Expectation Plots¶

Common pitfalls in interpretation of coefficients of linear models — Common pitfalls in interpretation of coefficients of linear models¶

Kernel Approximation¶

Examples concerning the sklearn.kernel_approximation module.

Scalable learning with polynomial kernel aproximation — Scalable learning with polynomial kernel aproximation¶

Manifold learning¶

Examples concerning the sklearn.manifold module.

Swiss Roll reduction with LLE — Swiss Roll reduction with LLE¶

Comparison of Manifold Learning methods — Comparison of Manifold Learning methods¶

Multi-dimensional scaling — Multi-dimensional scaling¶

t-SNE: The effect of various perplexity values on the shape — t-SNE: The effect of various perplexity values on the shape¶

Manifold Learning methods on a severed sphere — Manifold Learning methods on a severed sphere¶

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap... — Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…¶

Missing Value Imputation¶

Examples concerning the sklearn.impute module.

Imputing missing values with variants of IterativeImputer — Imputing missing values with variants of IterativeImputer¶

Imputing missing values before building an estimator — Imputing missing values before building an estimator¶

Model Selection¶

Examples related to the sklearn.model_selection module.

Plotting Cross-Validated Predictions — Plotting Cross-Validated Predictions¶

Confusion matrix — Confusion matrix¶

Plotting Validation Curves — Plotting Validation Curves¶

Detection error tradeoff (DET) curve — Detection error tradeoff (DET) curve¶

Successive Halving Iterations — Successive Halving Iterations¶

Underfitting vs. Overfitting — Underfitting vs. Overfitting¶

Parameter estimation using grid search with cross-validation — Parameter estimation using grid search with cross-validation¶

Comparing randomized search and grid search for hyperparameter estimation — Comparing randomized search and grid search for hyperparameter estimation¶

Train error vs Test error — Train error vs Test error¶

Receiver Operating Characteristic (ROC) with cross validation — Receiver Operating Characteristic (ROC) with cross validation¶

Nested versus non-nested cross-validation — Nested versus non-nested cross-validation¶

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV — Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV¶

Sample pipeline for text feature extraction and evaluation — Sample pipeline for text feature extraction and evaluation¶

Balance model complexity and cross-validated score — Balance model complexity and cross-validated score¶

Comparison between grid search and successive halving — Comparison between grid search and successive halving¶

Visualizing cross-validation behavior in scikit-learn — Visualizing cross-validation behavior in scikit-learn¶

Receiver Operating Characteristic (ROC) — Receiver Operating Characteristic (ROC)¶

Precision-Recall — Precision-Recall¶

Plotting Learning Curves — Plotting Learning Curves¶

Statistical comparison of models using grid search — Statistical comparison of models using grid search¶

Multioutput methods¶

Examples concerning the sklearn.multioutput module.

Classifier Chain — Classifier Chain¶

Neural Networks¶

Examples concerning the sklearn.neural_network module.

Visualization of MLP weights on MNIST — Visualization of MLP weights on MNIST¶

Restricted Boltzmann Machine features for digit classification — Restricted Boltzmann Machine features for digit classification¶

Compare Stochastic learning strategies for MLPClassifier — Compare Stochastic learning strategies for MLPClassifier¶

Varying regularization in Multi-layer Perceptron — Varying regularization in Multi-layer Perceptron¶

Pipelines and composite estimators¶

Examples of how to compose transformers and pipelines from other estimators. See the User Guide.

Concatenating multiple feature extraction methods — Concatenating multiple feature extraction methods¶

Pipelining: chaining a PCA and a logistic regression — Pipelining: chaining a PCA and a logistic regression¶

Selecting dimensionality reduction with Pipeline and GridSearchCV — Selecting dimensionality reduction with Pipeline and GridSearchCV¶

Column Transformer with Mixed Types — Column Transformer with Mixed Types¶

Column Transformer with Heterogeneous Data Sources — Column Transformer with Heterogeneous Data Sources¶

Effect of transforming the targets in regression model — Effect of transforming the targets in regression model¶

Preprocessing¶

Examples concerning the sklearn.preprocessing module.

Using KBinsDiscretizer to discretize continuous features — Using KBinsDiscretizer to discretize continuous features¶

Demonstrating the different strategies of KBinsDiscretizer — Demonstrating the different strategies of KBinsDiscretizer¶

Importance of Feature Scaling — Importance of Feature Scaling¶

Map data to a normal distribution — Map data to a normal distribution¶

Feature discretization — Feature discretization¶

Compare the effect of different scalers on data with outliers — Compare the effect of different scalers on data with outliers¶

Semi Supervised Classification¶

Examples concerning the sklearn.semi_supervised module.

Label Propagation learning a complex structure — Label Propagation learning a complex structure¶

Label Propagation digits: Demonstrating performance — Label Propagation digits: Demonstrating performance¶

Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset — Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset¶

Effect of varying threshold for self-training — Effect of varying threshold for self-training¶

Semi-supervised Classification on a Text Dataset — Semi-supervised Classification on a Text Dataset¶

Label Propagation digits active learning — Label Propagation digits active learning¶

Tutorial exercises¶

Exercises for the tutorials

Digits Classification Exercise — Digits Classification Exercise¶

Cross-validation on Digits Dataset Exercise — Cross-validation on Digits Dataset Exercise¶

SVM Exercise — SVM Exercise¶

Cross-validation on diabetes Dataset Exercise — Cross-validation on diabetes Dataset Exercise¶

Working with text documents¶

Examples concerning the sklearn.feature_extraction.text module.

FeatureHasher and DictVectorizer Comparison — FeatureHasher and DictVectorizer Comparison¶

Clustering text documents using k-means — Clustering text documents using k-means¶

Classification of text documents using sparse features — Classification of text documents using sparse features¶

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