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This is documentation for an old release of Scikit-learn (version 0.23). Try the latest stable release (version 1.6) or development (unstable) versions.

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 0.22

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.

Normal and Shrinkage Linear Discriminant Analysis for classification

Normal and Shrinkage Linear Discriminant Analysis for classification¶

Recognizing hand-written digits

Recognizing hand-written digits¶

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.

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¶

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

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

Comparing different hierarchical linkage methods on toy datasets

Comparing different hierarchical linkage methods on toy datasets¶

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.

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¶

Feature importances with forests of trees

Feature importances with forests of trees¶

Monotonic Constraints

Monotonic Constraints¶

IsolationForest example

IsolationForest example¶

Plot the decision boundaries of a VotingClassifier

Plot the decision boundaries of a VotingClassifier¶

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¶

Early stopping of Gradient Boosting

Early stopping of Gradient Boosting¶

Feature transformations with ensembles of trees

Feature transformations with ensembles of trees¶

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¶

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

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¶

Feature selection using SelectFromModel and LassoCV

Feature selection using SelectFromModel and LassoCV¶

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¶

SGD: Maximum margin separating hyperplane

SGD: Maximum margin separating hyperplane¶

Plot Ridge coefficients as a function of the regularization

Plot Ridge coefficients as a function of the regularization¶

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¶

Polynomial interpolation

Polynomial interpolation¶

Logistic function

Logistic function¶

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¶

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 Plots

Partial Dependence Plots¶

Common pitfalls in interpretation of coefficients of linear models

Common pitfalls in interpretation of coefficients of linear models¶

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¶

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¶

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¶

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 FunctionTransformer to select columns

Using FunctionTransformer to select columns¶

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.

Decision boundary of label propagation versus SVM on the Iris dataset

Decision boundary of label propagation versus SVM on the Iris dataset¶

Label Propagation learning a complex structure

Label Propagation learning a complex structure¶

Label Propagation digits: Demonstrating performance

Label Propagation digits: Demonstrating performance¶

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

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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