Examples

Release Highlights

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

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.2
Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.1
Release Highlights for scikit-learn 1.0

Release Highlights for scikit-learn 1.0

Release Highlights for scikit-learn 1.0
Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24
Release Highlights for scikit-learn 0.23

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

Release Highlights for scikit-learn 0.22

Biclustering

Examples concerning the sklearn.cluster.bicluster module.

A demo of the Spectral Biclustering algorithm

A demo of the Spectral Biclustering algorithm

A demo of the Spectral Biclustering algorithm
A demo of the Spectral Co-Clustering algorithm

A demo of the Spectral Co-Clustering algorithm

A demo of the Spectral Co-Clustering algorithm
Biclustering documents with the Spectral Co-clustering 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

Comparison of Calibration of Classifiers
Probability Calibration curves

Probability Calibration curves

Probability Calibration curves
Probability Calibration for 3-class classification

Probability Calibration for 3-class classification

Probability Calibration for 3-class classification
Probability calibration of classifiers

Probability calibration of classifiers

Probability calibration of classifiers

Classification

General examples about classification algorithms.

Classifier comparison

Classifier comparison

Classifier comparison
Linear and Quadratic Discriminant Analysis with covariance ellipsoid

Linear and Quadratic Discriminant Analysis with covariance ellipsoid

Linear and Quadratic Discriminant Analysis with covariance ellipsoid
Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification

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

Plot classification probability
Recognizing hand-written digits

Recognizing hand-written digits

Recognizing hand-written digits

Clustering

Examples concerning the sklearn.cluster module.

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

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

A demo of K-Means clustering on the handwritten digits data
A demo of structured Ward hierarchical clustering on an image of coins

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

A demo of structured Ward hierarchical clustering on an image of coins
A demo of the mean-shift clustering algorithm

A demo of the mean-shift clustering algorithm

A demo of the mean-shift clustering algorithm
Adjustment for chance in clustering performance evaluation

Adjustment for chance in clustering performance evaluation

Adjustment for chance in clustering performance evaluation
Agglomerative clustering with and without structure

Agglomerative clustering with and without structure

Agglomerative clustering with and without structure
Agglomerative clustering with different metrics

Agglomerative clustering with different metrics

Agglomerative clustering with different metrics
An example of K-Means++ initialization

An example of K-Means++ initialization

An example of K-Means++ initialization
Bisecting K-Means and Regular K-Means Performance Comparison

Bisecting K-Means and Regular K-Means Performance Comparison

Bisecting K-Means and Regular K-Means Performance Comparison
Color Quantization using K-Means

Color Quantization using K-Means

Color Quantization using K-Means
Compare BIRCH and MiniBatchKMeans

Compare BIRCH and MiniBatchKMeans

Compare BIRCH and MiniBatchKMeans
Comparing different clustering algorithms on toy datasets

Comparing different clustering algorithms on toy datasets

Comparing different clustering algorithms on toy datasets
Comparing different hierarchical linkage methods on toy datasets

Comparing different hierarchical linkage methods on toy datasets

Comparing different hierarchical linkage methods on toy datasets
Comparison of the K-Means and MiniBatchKMeans clustering algorithms

Comparison of the K-Means and MiniBatchKMeans clustering algorithms

Comparison of the K-Means and MiniBatchKMeans clustering algorithms
Demo of DBSCAN clustering algorithm

Demo of DBSCAN clustering algorithm

Demo of DBSCAN clustering algorithm
Demo of OPTICS clustering algorithm

Demo of OPTICS clustering algorithm

Demo of OPTICS clustering algorithm
Demo of affinity propagation clustering algorithm

Demo of affinity propagation clustering algorithm

Demo of affinity propagation clustering algorithm
Demonstration of k-means assumptions

Demonstration of k-means assumptions

Demonstration of k-means assumptions
Empirical evaluation of the impact of k-means initialization

Empirical evaluation of the impact of k-means initialization

Empirical evaluation of the impact of k-means initialization
Feature agglomeration

Feature agglomeration

Feature agglomeration
Feature agglomeration vs. univariate selection

Feature agglomeration vs. univariate selection

Feature agglomeration vs. univariate selection
Hierarchical clustering: structured vs unstructured ward

Hierarchical clustering: structured vs unstructured ward

Hierarchical clustering: structured vs unstructured ward
Inductive Clustering

Inductive Clustering

Inductive Clustering
K-means Clustering

K-means Clustering

K-means Clustering
Online learning of a dictionary of parts of faces

Online learning of a dictionary of parts of faces

Online learning of a dictionary of parts of faces
Plot Hierarchical Clustering Dendrogram

Plot Hierarchical Clustering Dendrogram

Plot Hierarchical Clustering Dendrogram
Segmenting the picture of greek coins in regions

Segmenting the picture of greek coins in regions

Segmenting the picture of greek coins in regions
Selecting the number of clusters with silhouette analysis on KMeans clustering

Selecting the number of clusters with silhouette analysis on KMeans clustering

Selecting the number of clusters with silhouette analysis on KMeans clustering
Spectral clustering for image segmentation

Spectral clustering for image segmentation

Spectral clustering for image segmentation
Various Agglomerative Clustering on a 2D embedding of digits

Various Agglomerative Clustering on a 2D embedding of digits

Various Agglomerative Clustering on a 2D embedding of digits
Vector Quantization Example

Vector Quantization Example

Vector Quantization Example

Covariance estimation

Examples concerning the sklearn.covariance module.

Ledoit-Wolf vs OAS estimation

Ledoit-Wolf vs OAS estimation

Ledoit-Wolf vs OAS estimation
Robust covariance estimation and Mahalanobis distances relevance

Robust covariance estimation and Mahalanobis distances relevance

Robust covariance estimation and Mahalanobis distances relevance
Robust vs Empirical covariance estimate

Robust vs Empirical covariance estimate

Robust vs Empirical covariance estimate
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
Sparse inverse covariance estimation

Sparse inverse covariance estimation

Sparse inverse covariance estimation

Cross decomposition

Examples concerning the sklearn.cross_decomposition module.

Compare cross decomposition methods

Compare cross decomposition methods

Compare cross decomposition methods
Principal Component Regression vs Partial Least Squares Regression

Principal Component Regression vs Partial Least Squares Regression

Principal Component Regression vs Partial Least Squares Regression

Dataset examples

Examples concerning the sklearn.datasets module.

Plot randomly generated classification dataset

Plot randomly generated classification dataset

Plot randomly generated classification dataset
Plot randomly generated multilabel dataset

Plot randomly generated multilabel dataset

Plot randomly generated multilabel dataset
The Digit Dataset

The Digit Dataset

The Digit Dataset
The Iris Dataset

The Iris Dataset

The Iris Dataset

Decision Trees

Examples concerning the sklearn.tree module.

Decision Tree Regression

Decision Tree Regression

Decision Tree Regression
Multi-output Decision Tree Regression

Multi-output Decision Tree Regression

Multi-output Decision Tree Regression
Plot the decision surface of decision trees trained on the iris dataset

Plot the decision surface of decision trees trained on the iris dataset

Plot the decision surface of decision trees trained on the iris dataset
Post pruning decision trees with cost complexity pruning

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

Understanding the decision tree structure

Decomposition

Examples concerning the sklearn.decomposition module.

Beta-divergence loss functions

Beta-divergence loss functions

Beta-divergence loss functions
Blind source separation using FastICA

Blind source separation using FastICA

Blind source separation using FastICA
Comparison of LDA and PCA 2D projection of Iris dataset

Comparison of LDA and PCA 2D projection of Iris dataset

Comparison of LDA and PCA 2D projection of Iris dataset
Faces dataset decompositions

Faces dataset decompositions

Faces dataset decompositions
Factor Analysis (with rotation) to visualize patterns

Factor Analysis (with rotation) to visualize patterns

Factor Analysis (with rotation) to visualize patterns
FastICA on 2D point clouds

FastICA on 2D point clouds

FastICA on 2D point clouds
Image denoising using dictionary learning

Image denoising using dictionary learning

Image denoising using dictionary learning
Incremental PCA

Incremental PCA

Incremental PCA
Kernel PCA

Kernel PCA

Kernel PCA
Model selection with Probabilistic PCA and Factor Analysis (FA)

Model selection with Probabilistic PCA and Factor Analysis (FA)

Model selection with Probabilistic PCA and Factor Analysis (FA)
PCA example with Iris Data-set

PCA example with Iris Data-set

PCA example with Iris Data-set
Principal components analysis (PCA)

Principal components analysis (PCA)

Principal components analysis (PCA)
Sparse coding with a precomputed dictionary

Sparse coding with a precomputed dictionary

Sparse coding with a precomputed dictionary

Ensemble methods

Examples concerning the sklearn.ensemble module.

Categorical Feature Support in Gradient Boosting

Categorical Feature Support in Gradient Boosting

Categorical Feature Support in Gradient Boosting
Combine predictors using stacking

Combine predictors using stacking

Combine predictors using stacking
Comparing random forests and the multi-output meta estimator

Comparing random forests and the multi-output meta estimator

Comparing random forests and the multi-output meta estimator
Decision Tree Regression with AdaBoost

Decision Tree Regression with AdaBoost

Decision Tree Regression with AdaBoost
Discrete versus Real AdaBoost

Discrete versus Real AdaBoost

Discrete versus Real AdaBoost
Early stopping of Gradient Boosting

Early stopping of Gradient Boosting

Early stopping of Gradient Boosting
Feature importances with a forest of trees

Feature importances with a forest of trees

Feature importances with a forest of trees
Feature transformations with ensembles of trees

Feature transformations with ensembles of trees

Feature transformations with ensembles of trees
Gradient Boosting Out-of-Bag estimates

Gradient Boosting Out-of-Bag estimates

Gradient Boosting Out-of-Bag estimates
Gradient Boosting regression

Gradient Boosting regression

Gradient Boosting regression
Gradient Boosting regularization

Gradient Boosting regularization

Gradient Boosting regularization
Hashing feature transformation using Totally Random Trees

Hashing feature transformation using Totally Random Trees

Hashing feature transformation using Totally Random Trees
IsolationForest example

IsolationForest example

IsolationForest example
Monotonic Constraints

Monotonic Constraints

Monotonic Constraints
Multi-class AdaBoosted Decision Trees

Multi-class AdaBoosted Decision Trees

Multi-class AdaBoosted Decision Trees
OOB Errors for Random Forests

OOB Errors for Random Forests

OOB Errors for Random Forests
Pixel importances with a parallel forest of trees

Pixel importances with a parallel forest of trees

Pixel importances with a parallel forest of trees
Plot class probabilities calculated by the VotingClassifier

Plot class probabilities calculated by the VotingClassifier

Plot class probabilities calculated by the VotingClassifier
Plot individual and voting regression predictions

Plot individual and voting regression predictions

Plot individual and voting regression predictions
Plot the decision boundaries of a VotingClassifier

Plot the decision boundaries of a VotingClassifier

Plot the decision boundaries of a VotingClassifier
Plot the decision surfaces of ensembles of trees on the iris dataset

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

Plot the decision surfaces of ensembles of trees on the iris dataset
Prediction Intervals for Gradient Boosting Regression

Prediction Intervals for Gradient Boosting Regression

Prediction Intervals for Gradient Boosting Regression
Single estimator versus bagging: bias-variance decomposition

Single estimator versus bagging: bias-variance decomposition

Single estimator versus bagging: bias-variance decomposition
Two-class AdaBoost

Two-class AdaBoost

Two-class AdaBoost

Examples based on real world datasets

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

Compressive sensing: tomography reconstruction with L1 prior (Lasso)

Compressive sensing: tomography reconstruction with L1 prior (Lasso)

Compressive sensing: tomography reconstruction with L1 prior (Lasso)
Faces recognition example using eigenfaces and SVMs

Faces recognition example using eigenfaces and SVMs

Faces recognition example using eigenfaces and SVMs
Image denoising using kernel PCA

Image denoising using kernel PCA

Image denoising using kernel PCA
Libsvm GUI

Libsvm GUI

Libsvm GUI
Model Complexity Influence

Model Complexity Influence

Model Complexity Influence
Out-of-core classification of text documents

Out-of-core classification of text documents

Out-of-core classification of text documents
Outlier detection on a real data set

Outlier detection on a real data set

Outlier detection on a real data set
Prediction Latency

Prediction Latency

Prediction Latency
Species distribution modeling

Species distribution modeling

Species distribution modeling
Time-related feature engineering

Time-related feature engineering

Time-related feature engineering
Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation

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

Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
Visualizing the stock market structure

Visualizing the stock market structure

Visualizing the stock market structure
Wikipedia principal eigenvector

Wikipedia principal eigenvector

Wikipedia principal eigenvector

Feature Selection

Examples concerning the sklearn.feature_selection module.

Comparison of F-test and mutual information

Comparison of F-test and mutual information

Comparison of F-test and mutual information
Model-based and sequential feature selection

Model-based and sequential feature selection

Model-based and sequential feature selection
Pipeline ANOVA SVM

Pipeline ANOVA SVM

Pipeline ANOVA SVM
Recursive feature elimination

Recursive feature elimination

Recursive feature elimination
Recursive feature elimination with cross-validation

Recursive feature elimination with cross-validation

Recursive feature elimination with cross-validation
Univariate Feature Selection

Univariate Feature Selection

Univariate Feature Selection

Gaussian Mixture Models

Examples concerning the sklearn.mixture module.

Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture

Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture

Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture
Density Estimation for a Gaussian mixture

Density Estimation for a Gaussian mixture

Density Estimation for a Gaussian mixture
GMM Initialization Methods

GMM Initialization Methods

GMM Initialization Methods
GMM covariances

GMM covariances

GMM covariances
Gaussian Mixture Model Ellipsoids

Gaussian Mixture Model Ellipsoids

Gaussian Mixture Model Ellipsoids
Gaussian Mixture Model Selection

Gaussian Mixture Model Selection

Gaussian Mixture Model Selection
Gaussian Mixture Model Sine Curve

Gaussian Mixture Model Sine Curve

Gaussian Mixture Model Sine Curve

Gaussian Process for Machine Learning

Examples concerning the sklearn.gaussian_process module.

Comparison of kernel ridge and Gaussian process regression

Comparison of kernel ridge and Gaussian process regression

Comparison of kernel ridge and Gaussian process regression
Gaussian Processes regression: basic introductory example

Gaussian Processes regression: basic introductory example

Gaussian Processes regression: basic introductory example
Gaussian process classification (GPC) on iris dataset

Gaussian process classification (GPC) on iris dataset

Gaussian process classification (GPC) on iris dataset
Gaussian process regression (GPR) on Mauna Loa CO2 data

Gaussian process regression (GPR) on Mauna Loa CO2 data

Gaussian process regression (GPR) on Mauna Loa CO2 data
Gaussian process regression (GPR) with noise-level estimation

Gaussian process regression (GPR) with noise-level estimation

Gaussian process regression (GPR) with noise-level estimation
Gaussian processes on discrete data structures

Gaussian processes on discrete data structures

Gaussian processes on discrete data structures
Illustration of Gaussian process classification (GPC) on the XOR dataset

Illustration of Gaussian process classification (GPC) on the XOR dataset

Illustration of Gaussian process classification (GPC) on the XOR dataset
Illustration of prior and posterior Gaussian process for different kernels

Illustration of prior and posterior Gaussian process for different kernels

Illustration of prior and posterior Gaussian process for different kernels
Iso-probability lines for Gaussian Processes classification (GPC)

Iso-probability lines for Gaussian Processes classification (GPC)

Iso-probability lines for Gaussian Processes classification (GPC)
Probabilistic predictions with Gaussian process classification (GPC)

Probabilistic predictions with Gaussian process classification (GPC)

Probabilistic predictions with Gaussian process classification (GPC)

Generalized Linear Models

Examples concerning the sklearn.linear_model module.

Comparing Linear Bayesian Regressors

Comparing Linear Bayesian Regressors

Comparing Linear Bayesian Regressors
Comparing various online solvers

Comparing various online solvers

Comparing various online solvers
Curve Fitting with Bayesian Ridge Regression

Curve Fitting with Bayesian Ridge Regression

Curve Fitting with Bayesian Ridge Regression
Early stopping of Stochastic Gradient Descent

Early stopping of Stochastic Gradient Descent

Early stopping of Stochastic Gradient Descent
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples

Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples

Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples
HuberRegressor vs Ridge on dataset with strong outliers

HuberRegressor vs Ridge on dataset with strong outliers

HuberRegressor vs Ridge on dataset with strong outliers
Joint feature selection with multi-task Lasso

Joint feature selection with multi-task Lasso

Joint feature selection with multi-task Lasso
L1 Penalty and Sparsity in Logistic Regression

L1 Penalty and Sparsity in Logistic Regression

L1 Penalty and Sparsity in Logistic Regression
Lasso and Elastic Net

Lasso and Elastic Net

Lasso and Elastic Net
Lasso and Elastic Net for Sparse Signals

Lasso and Elastic Net for Sparse Signals

Lasso and Elastic Net for Sparse Signals
Lasso model selection via information criteria

Lasso model selection via information criteria

Lasso model selection via information criteria
Lasso model selection: AIC-BIC / cross-validation

Lasso model selection: AIC-BIC / cross-validation

Lasso model selection: AIC-BIC / cross-validation
Lasso on dense and sparse data

Lasso on dense and sparse data

Lasso on dense and sparse data
Lasso path using LARS

Lasso path using LARS

Lasso path using LARS
Linear Regression Example

Linear Regression Example

Linear Regression Example
Logistic Regression 3-class Classifier

Logistic Regression 3-class Classifier

Logistic Regression 3-class Classifier
Logistic function

Logistic function

Logistic function
MNIST classification using multinomial logistic + L1

MNIST classification using multinomial logistic + L1

MNIST classification using multinomial logistic + L1
Multiclass sparse logistic regression on 20newgroups

Multiclass sparse logistic regression on 20newgroups

Multiclass sparse logistic regression on 20newgroups
Non-negative least squares

Non-negative least squares

Non-negative least squares
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent

One-Class SVM versus One-Class SVM using Stochastic Gradient Descent

One-Class SVM versus One-Class SVM using Stochastic Gradient Descent
Ordinary Least Squares and Ridge Regression Variance

Ordinary Least Squares and Ridge Regression Variance

Ordinary Least Squares and Ridge Regression Variance
Orthogonal Matching Pursuit

Orthogonal Matching Pursuit

Orthogonal Matching Pursuit
Plot Ridge coefficients as a function of the L2 regularization

Plot Ridge coefficients as a function of the L2 regularization

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

Plot Ridge coefficients as a function of the regularization

Plot Ridge coefficients as a function of the regularization
Plot multi-class SGD on the iris dataset

Plot multi-class SGD on the iris dataset

Plot multi-class SGD on the iris dataset
Plot multinomial and One-vs-Rest Logistic Regression

Plot multinomial and One-vs-Rest Logistic Regression

Plot multinomial and One-vs-Rest Logistic Regression
Poisson regression and non-normal loss

Poisson regression and non-normal loss

Poisson regression and non-normal loss
Polynomial and Spline interpolation

Polynomial and Spline interpolation

Polynomial and Spline interpolation
Quantile regression

Quantile regression

Quantile regression
Regularization path of L1- Logistic Regression

Regularization path of L1- Logistic Regression

Regularization path of L1- Logistic Regression
Robust linear estimator fitting

Robust linear estimator fitting

Robust linear estimator fitting
Robust linear model estimation using RANSAC

Robust linear model estimation using RANSAC

Robust linear model estimation using RANSAC
SGD: Maximum margin separating hyperplane

SGD: Maximum margin separating hyperplane

SGD: Maximum margin separating hyperplane
SGD: Penalties

SGD: Penalties

SGD: Penalties
SGD: Weighted samples

SGD: Weighted samples

SGD: Weighted samples
SGD: convex loss functions

SGD: convex loss functions

SGD: convex loss functions
Sparsity Example: Fitting only features 1  and 2

Sparsity Example: Fitting only features 1 and 2

Sparsity Example: Fitting only features 1 and 2
Theil-Sen Regression

Theil-Sen Regression

Theil-Sen Regression
Tweedie regression on insurance claims

Tweedie regression on insurance claims

Tweedie regression on insurance claims

Inspection

Examples related to the sklearn.inspection module.

Common pitfalls in the interpretation of coefficients of linear models

Common pitfalls in the interpretation of coefficients of linear models

Common pitfalls in the interpretation of coefficients of linear models
Partial Dependence and Individual Conditional Expectation Plots

Partial Dependence and Individual Conditional Expectation Plots

Partial Dependence and Individual Conditional Expectation Plots
Permutation Importance vs Random Forest Feature Importance (MDI)

Permutation Importance vs Random Forest Feature Importance (MDI)

Permutation Importance vs Random Forest Feature Importance (MDI)
Permutation Importance with Multicollinear or Correlated Features

Permutation Importance with Multicollinear or Correlated Features

Permutation Importance with Multicollinear or Correlated Features

Kernel Approximation

Examples concerning the sklearn.kernel_approximation module.

Scalable learning with polynomial kernel approximation

Scalable learning with polynomial kernel approximation

Scalable learning with polynomial kernel approximation

Manifold learning

Examples concerning the sklearn.manifold module.

Comparison of Manifold Learning methods

Comparison of Manifold Learning methods

Comparison of Manifold Learning methods
Manifold Learning methods on a severed sphere

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…

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...
Multi-dimensional scaling

Multi-dimensional scaling

Multi-dimensional scaling
Swiss Roll And Swiss-Hole Reduction

Swiss Roll And Swiss-Hole Reduction

Swiss Roll And Swiss-Hole Reduction
t-SNE: The effect of various perplexity values on the shape

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

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

Miscellaneous

Miscellaneous and introductory examples for scikit-learn.

Advanced Plotting With Partial Dependence

Advanced Plotting With Partial Dependence

Advanced Plotting With Partial Dependence
Comparing anomaly detection algorithms for outlier detection on toy datasets

Comparing anomaly detection algorithms for outlier detection on toy datasets

Comparing anomaly detection algorithms for outlier detection on toy datasets
Comparison of kernel ridge regression and SVR

Comparison of kernel ridge regression and SVR

Comparison of kernel ridge regression and SVR
Displaying Pipelines

Displaying Pipelines

Displaying Pipelines
Displaying estimators and complex pipelines

Displaying estimators and complex pipelines

Displaying estimators and complex pipelines
Evaluation of outlier detection estimators

Evaluation of outlier detection estimators

Evaluation of outlier detection estimators
Explicit feature map approximation for RBF kernels

Explicit feature map approximation for RBF kernels

Explicit feature map approximation for RBF kernels
Face completion with a multi-output estimators

Face completion with a multi-output estimators

Face completion with a multi-output estimators
Introducing the `set_output` API

Introducing the set_output API

Introducing the `set_output` API
Isotonic Regression

Isotonic Regression

Isotonic Regression
Multilabel classification

Multilabel classification

Multilabel classification
ROC Curve with Visualization API

ROC Curve with Visualization API

ROC Curve with Visualization API
The Johnson-Lindenstrauss bound for embedding with random projections

The Johnson-Lindenstrauss bound for embedding with random projections

The Johnson-Lindenstrauss bound for embedding with random projections
Visualizations with Display Objects

Visualizations with Display Objects

Visualizations with Display Objects

Missing Value Imputation

Examples concerning the sklearn.impute module.

Imputing missing values before building an estimator

Imputing missing values before building an estimator

Imputing missing values before building an estimator
Imputing missing values with variants of IterativeImputer

Imputing missing values with variants of IterativeImputer

Imputing missing values with variants of IterativeImputer

Model Selection

Examples related to the sklearn.model_selection module.

Balance model complexity and cross-validated score

Balance model complexity and cross-validated score

Balance model complexity and cross-validated score
Class Likelihood Ratios to measure classification performance

Class Likelihood Ratios to measure classification performance

Class Likelihood Ratios to measure classification performance
Comparing randomized search and grid search for hyperparameter estimation

Comparing randomized search and grid search for hyperparameter estimation

Comparing randomized search and grid search for hyperparameter estimation
Comparison between grid search and successive halving

Comparison between grid search and successive halving

Comparison between grid search and successive halving
Confusion matrix

Confusion matrix

Confusion matrix
Custom refit strategy of a grid search with cross-validation

Custom refit strategy of a grid search with cross-validation

Custom refit strategy of a grid search with cross-validation
Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV
Detection error tradeoff (DET) curve

Detection error tradeoff (DET) curve

Detection error tradeoff (DET) curve
Multiclass Receiver Operating Characteristic (ROC)

Multiclass Receiver Operating Characteristic (ROC)

Multiclass Receiver Operating Characteristic (ROC)
Nested versus non-nested cross-validation

Nested versus non-nested cross-validation

Nested versus non-nested cross-validation
Plotting Cross-Validated Predictions

Plotting Cross-Validated Predictions

Plotting Cross-Validated Predictions
Plotting Learning Curves and Checking Models' Scalability

Plotting Learning Curves and Checking Models’ Scalability

Plotting Learning Curves and Checking Models' Scalability
Plotting Validation Curves

Plotting Validation Curves

Plotting Validation Curves
Precision-Recall

Precision-Recall

Precision-Recall
Receiver Operating Characteristic (ROC) with cross validation

Receiver Operating Characteristic (ROC) with cross validation

Receiver Operating Characteristic (ROC) with cross validation
Sample pipeline for text feature extraction and evaluation

Sample pipeline for text feature extraction and evaluation

Sample pipeline for text feature extraction and evaluation
Statistical comparison of models using grid search

Statistical comparison of models using grid search

Statistical comparison of models using grid search
Successive Halving Iterations

Successive Halving Iterations

Successive Halving Iterations
Test with permutations the significance of a classification score

Test with permutations the significance of a classification score

Test with permutations the significance of a classification score
Train error vs Test error

Train error vs Test error

Train error vs Test error
Underfitting vs. Overfitting

Underfitting vs. Overfitting

Underfitting vs. Overfitting
Visualizing cross-validation behavior in scikit-learn

Visualizing cross-validation behavior in scikit-learn

Visualizing cross-validation behavior in scikit-learn

Multioutput methods

Examples concerning the sklearn.multioutput module.

Classifier Chain

Classifier Chain

Classifier Chain

Nearest Neighbors

Examples concerning the sklearn.neighbors module.

Approximate nearest neighbors in TSNE

Approximate nearest neighbors in TSNE

Approximate nearest neighbors in TSNE
Caching nearest neighbors

Caching nearest neighbors

Caching nearest neighbors
Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Comparing Nearest Neighbors with and without Neighborhood Components Analysis
Dimensionality Reduction with Neighborhood Components Analysis

Dimensionality Reduction with Neighborhood Components Analysis

Dimensionality Reduction with Neighborhood Components Analysis
Kernel Density Estimate of Species Distributions

Kernel Density Estimate of Species Distributions

Kernel Density Estimate of Species Distributions
Kernel Density Estimation

Kernel Density Estimation

Kernel Density Estimation
Nearest Centroid Classification

Nearest Centroid Classification

Nearest Centroid Classification
Nearest Neighbors Classification

Nearest Neighbors Classification

Nearest Neighbors Classification
Nearest Neighbors regression

Nearest Neighbors regression

Nearest Neighbors regression
Neighborhood Components Analysis Illustration

Neighborhood Components Analysis Illustration

Neighborhood Components Analysis Illustration
Novelty detection with Local Outlier Factor (LOF)

Novelty detection with Local Outlier Factor (LOF)

Novelty detection with Local Outlier Factor (LOF)
Outlier detection with Local Outlier Factor (LOF)

Outlier detection with Local Outlier Factor (LOF)

Outlier detection with Local Outlier Factor (LOF)
Simple 1D Kernel Density Estimation

Simple 1D Kernel Density Estimation

Simple 1D Kernel Density Estimation

Neural Networks

Examples concerning the sklearn.neural_network module.

Compare Stochastic learning strategies for MLPClassifier

Compare Stochastic learning strategies for MLPClassifier

Compare Stochastic learning strategies for MLPClassifier
Restricted Boltzmann Machine features for digit classification

Restricted Boltzmann Machine features for digit classification

Restricted Boltzmann Machine features for digit classification
Varying regularization in Multi-layer Perceptron

Varying regularization in Multi-layer Perceptron

Varying regularization in Multi-layer Perceptron
Visualization of MLP weights on MNIST

Visualization of MLP weights on MNIST

Visualization of MLP weights on MNIST

Pipelines and composite estimators

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

Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources
Column Transformer with Mixed Types

Column Transformer with Mixed Types

Column Transformer with Mixed Types
Concatenating multiple feature extraction methods

Concatenating multiple feature extraction methods

Concatenating multiple feature extraction methods
Effect of transforming the targets in regression model

Effect of transforming the targets in regression model

Effect of transforming the targets in regression model
Pipelining: chaining a PCA and a logistic regression

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

Selecting dimensionality reduction with Pipeline and GridSearchCV

Preprocessing

Examples concerning the sklearn.preprocessing module.

Compare the effect of different scalers on data with outliers

Compare the effect of different scalers on data with outliers

Compare the effect of different scalers on data with outliers
Demonstrating the different strategies of KBinsDiscretizer

Demonstrating the different strategies of KBinsDiscretizer

Demonstrating the different strategies of KBinsDiscretizer
Feature discretization

Feature discretization

Feature discretization
Importance of Feature Scaling

Importance of Feature Scaling

Importance of Feature Scaling
Map data to a normal distribution

Map data to a normal distribution

Map data to a normal distribution
Using KBinsDiscretizer to discretize continuous features

Using KBinsDiscretizer to discretize continuous features

Using KBinsDiscretizer to discretize continuous features

Semi Supervised Classification

Examples concerning the sklearn.semi_supervised module.

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

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

Effect of varying threshold for self-training
Label Propagation digits active learning

Label Propagation digits active learning

Label Propagation digits active learning
Label Propagation digits: Demonstrating performance

Label Propagation digits: Demonstrating performance

Label Propagation digits: Demonstrating performance
Label Propagation learning a complex structure

Label Propagation learning a complex structure

Label Propagation learning a complex structure
Semi-supervised Classification on a Text Dataset

Semi-supervised Classification on a Text Dataset

Semi-supervised Classification on a Text Dataset

Support Vector Machines

Examples concerning the sklearn.svm module.

Non-linear SVM

Non-linear SVM

Non-linear SVM
One-class SVM with non-linear kernel (RBF)

One-class SVM with non-linear kernel (RBF)

One-class SVM with non-linear kernel (RBF)
Plot different SVM classifiers in the iris dataset

Plot different SVM classifiers in the iris dataset

Plot different SVM classifiers in the iris dataset
Plot the support vectors in LinearSVC

Plot the support vectors in LinearSVC

Plot the support vectors in LinearSVC
RBF SVM parameters

RBF SVM parameters

RBF SVM parameters
SVM Margins Example

SVM Margins Example

SVM Margins Example
SVM Tie Breaking Example

SVM Tie Breaking Example

SVM Tie Breaking Example
SVM with custom kernel

SVM with custom kernel

SVM with custom kernel
SVM-Anova: SVM with univariate feature selection

SVM-Anova: SVM with univariate feature selection

SVM-Anova: SVM with univariate feature selection
SVM-Kernels

SVM-Kernels

SVM-Kernels
SVM: Maximum margin separating hyperplane

SVM: Maximum margin separating hyperplane

SVM: Maximum margin separating hyperplane
SVM: Separating hyperplane for unbalanced classes

SVM: Separating hyperplane for unbalanced classes

SVM: Separating hyperplane for unbalanced classes
SVM: Weighted samples

SVM: Weighted samples

SVM: Weighted samples
Scaling the regularization parameter for SVCs

Scaling the regularization parameter for SVCs

Scaling the regularization parameter for SVCs
Support Vector Regression (SVR) using linear and non-linear kernels

Support Vector Regression (SVR) using linear and non-linear kernels

Support Vector Regression (SVR) using linear and non-linear kernels

Tutorial exercises

Exercises for the tutorials

Cross-validation on Digits Dataset Exercise

Cross-validation on Digits Dataset Exercise

Cross-validation on Digits Dataset Exercise
Cross-validation on diabetes Dataset Exercise

Cross-validation on diabetes Dataset Exercise

Cross-validation on diabetes Dataset Exercise
Digits Classification Exercise

Digits Classification Exercise

Digits Classification Exercise
SVM Exercise

SVM Exercise

SVM Exercise

Working with text documents

Examples concerning the sklearn.feature_extraction.text module.

Classification of text documents using sparse features

Classification of text documents using sparse features

Classification of text documents using sparse features
Clustering text documents using k-means

Clustering text documents using k-means

Clustering text documents using k-means
FeatureHasher and DictVectorizer Comparison

FeatureHasher and DictVectorizer Comparison

FeatureHasher and DictVectorizer Comparison

Gallery generated by Sphinx-Gallery