# Examples¶

## Release Highlights¶

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

Release Highlights for scikit-learn 1.4

Release Highlights for scikit-learn 1.3

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.0

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.22

## Biclustering¶

Examples concerning biclustering techniques.

A demo of the Spectral Biclustering algorithm

A demo of 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

Probability Calibration curves

Probability Calibration for 3-class classification

Probability calibration of classifiers

## Classification¶

General examples about classification algorithms.

Linear and Quadratic Discriminant Analysis with covariance ellipsoid

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

Plot classification probability

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 structured Ward hierarchical clustering on an image of coins

A demo of the mean-shift clustering algorithm

Adjustment for chance in clustering performance evaluation

Agglomerative clustering with and without structure

Agglomerative clustering with different metrics

An example of K-Means++ initialization

Bisecting K-Means and Regular K-Means Performance Comparison

Color Quantization using K-Means

Compare BIRCH and MiniBatchKMeans

Comparing different clustering algorithms on toy datasets

Comparing different hierarchical linkage methods on toy datasets

Comparison of the K-Means and MiniBatchKMeans clustering algorithms

Demo of DBSCAN clustering algorithm

Demo of HDBSCAN clustering algorithm

Demo of OPTICS clustering algorithm

Demo of affinity propagation clustering algorithm

Demonstration of k-means assumptions

Empirical evaluation of the impact of k-means initialization

Feature agglomeration vs. univariate selection

Hierarchical clustering: structured vs unstructured ward

Online learning of a dictionary of parts of faces

Plot Hierarchical Clustering Dendrogram

Segmenting the picture of greek coins in regions

Selecting the number of clusters with silhouette analysis on KMeans clustering

Spectral clustering for image segmentation

Various Agglomerative Clustering on a 2D embedding of digits

## Covariance estimation¶

Examples concerning the `sklearn.covariance`

module.

Robust covariance estimation and Mahalanobis distances relevance

Robust vs Empirical covariance estimate

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood

Sparse inverse covariance estimation

## Cross decomposition¶

Examples concerning the `sklearn.cross_decomposition`

module.

Compare cross decomposition methods

Principal Component Regression vs Partial Least Squares Regression

## Dataset examples¶

Examples concerning the `sklearn.datasets`

module.

Plot randomly generated classification dataset

Plot randomly generated multilabel dataset

## Decision Trees¶

Examples concerning the `sklearn.tree`

module.

Multi-output Decision Tree Regression

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

Post pruning decision trees with cost complexity pruning

Understanding the decision tree structure

## Decomposition¶

Examples concerning the `sklearn.decomposition`

module.

Blind source separation using FastICA

Comparison of LDA and PCA 2D projection of Iris dataset

Factor Analysis (with rotation) to visualize patterns

Image denoising using dictionary learning

Model selection with Probabilistic PCA and Factor Analysis (FA)

PCA example with Iris Data-set

Sparse coding with a precomputed dictionary

## Developing Estimators¶

Examples concerning the development of Custom Estimator.

__sklearn_is_fitted__ as Developer API

## Ensemble methods¶

Examples concerning the `sklearn.ensemble`

module.

Categorical Feature Support in Gradient Boosting

Combine predictors using stacking

Comparing Random Forests and Histogram Gradient Boosting models

Comparing random forests and the multi-output meta estimator

Decision Tree Regression with AdaBoost

Early stopping in Gradient Boosting

Feature importances with a forest of trees

Feature transformations with ensembles of trees

Gradient Boosting Out-of-Bag estimates

Gradient Boosting regularization

Hashing feature transformation using Totally Random Trees

Multi-class AdaBoosted Decision Trees

Pixel importances with a parallel forest of trees

Plot class probabilities calculated by the VotingClassifier

Plot individual and voting regression predictions

Plot the decision boundaries of a VotingClassifier

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

Prediction Intervals for Gradient Boosting Regression

Single estimator versus bagging: bias-variance decomposition

## 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)

Faces recognition example using eigenfaces and SVMs

Image denoising using kernel PCA

Lagged features for time series forecasting

Out-of-core classification of text documents

Outlier detection on a real data set

Time-related feature engineering

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

Visualizing the stock market structure

Wikipedia principal eigenvector

## Feature Selection¶

Examples concerning the `sklearn.feature_selection`

module.

Comparison of F-test and mutual information

Model-based and sequential feature selection

Recursive feature elimination with cross-validation

## Gaussian Mixture Models¶

Examples concerning the `sklearn.mixture`

module.

Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture

Density Estimation for a Gaussian mixture

Gaussian Mixture Model Ellipsoids

Gaussian Mixture Model Selection

Gaussian Mixture Model Sine Curve

## Gaussian Process for Machine Learning¶

Examples concerning the `sklearn.gaussian_process`

module.

Ability of Gaussian process regression (GPR) to estimate data noise-level

Comparison of kernel ridge and Gaussian process regression

Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)

Gaussian Processes regression: basic introductory example

Gaussian process classification (GPC) on iris dataset

Gaussian processes on discrete data structures

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

Illustration of prior and posterior Gaussian process for different kernels

Iso-probability lines for Gaussian Processes classification (GPC)

Probabilistic predictions with Gaussian process classification (GPC)

## Generalized Linear Models¶

Examples concerning the `sklearn.linear_model`

module.

Comparing Linear Bayesian Regressors

Comparing various online solvers

Curve Fitting with Bayesian Ridge Regression

Early stopping of Stochastic Gradient Descent

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

HuberRegressor vs Ridge on dataset with strong outliers

Joint feature selection with multi-task Lasso

L1 Penalty and Sparsity in Logistic Regression

L1-based models for Sparse Signals

Lasso model selection via information criteria

Lasso model selection: AIC-BIC / cross-validation

Lasso on dense and sparse data

Logistic Regression 3-class Classifier

MNIST classification using multinomial logistic + L1

Multiclass sparse logistic regression on 20newgroups

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

Ordinary Least Squares and Ridge Regression Variance

Plot Ridge coefficients as a function of the regularization

Plot multi-class SGD on the iris dataset

Plot multinomial and One-vs-Rest Logistic Regression

Poisson regression and non-normal loss

Polynomial and Spline interpolation

Regularization path of L1- Logistic Regression

Ridge coefficients as a function of the L2 Regularization

Robust linear estimator fitting

Robust linear model estimation using RANSAC

SGD: Maximum margin separating hyperplane

Sparsity Example: Fitting only features 1 and 2

Tweedie regression on insurance claims

## Inspection¶

Examples related to the `sklearn.inspection`

module.

Common pitfalls in the interpretation of coefficients of linear models

Failure of Machine Learning to infer causal effects

Partial Dependence and Individual Conditional Expectation Plots

Permutation Importance vs Random Forest Feature Importance (MDI)

Permutation Importance with Multicollinear or Correlated Features

## Kernel Approximation¶

Examples concerning the `sklearn.kernel_approximation`

module.

Scalable learning with polynomial kernel approximation

## Manifold learning¶

Examples concerning the `sklearn.manifold`

module.

Comparison of Manifold Learning methods

Manifold Learning methods on a severed sphere

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

Swiss Roll And Swiss-Hole Reduction

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

## Miscellaneous¶

Miscellaneous and introductory examples for scikit-learn.

Advanced Plotting With Partial Dependence

Comparing anomaly detection algorithms for outlier detection on toy datasets

Comparison of kernel ridge regression and SVR

Displaying estimators and complex pipelines

Evaluation of outlier detection estimators

Explicit feature map approximation for RBF kernels

Face completion with a multi-output estimators

Introducing the set_output API

ROC Curve with Visualization API

The Johnson-Lindenstrauss bound for embedding with random projections

Visualizations with Display Objects

## Missing Value Imputation¶

Examples concerning the `sklearn.impute`

module.

Imputing missing values before building an estimator

Imputing missing values with variants of IterativeImputer

## Model Selection¶

Examples related to the `sklearn.model_selection`

module.

Balance model complexity and cross-validated score

Class Likelihood Ratios to measure classification performance

Comparing randomized search and grid search for hyperparameter estimation

Comparison between grid search and successive halving

Custom refit strategy of a grid search with cross-validation

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

Detection error tradeoff (DET) curve

Multiclass Receiver Operating Characteristic (ROC)

Nested versus non-nested cross-validation

Plotting Cross-Validated Predictions

Plotting Learning Curves and Checking Models’ Scalability

Receiver Operating Characteristic (ROC) with cross validation

Sample pipeline for text feature extraction and evaluation

Statistical comparison of models using grid search

Test with permutations the significance of a classification score

Visualizing cross-validation behavior in scikit-learn

## Multiclass methods¶

Examples concerning the `sklearn.multiclass`

module.

Overview of multiclass training meta-estimators

## Multioutput methods¶

Examples concerning the `sklearn.multioutput`

module.

Multilabel classification using a classifier chain

## Nearest Neighbors¶

Examples concerning the `sklearn.neighbors`

module.

Approximate nearest neighbors in TSNE

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Dimensionality Reduction with Neighborhood Components Analysis

Kernel Density Estimate of Species Distributions

Nearest Centroid Classification

Nearest Neighbors Classification

Neighborhood Components Analysis Illustration

Novelty detection with Local Outlier Factor (LOF)

Outlier detection with Local Outlier Factor (LOF)

Simple 1D Kernel Density Estimation

## Neural Networks¶

Examples concerning the `sklearn.neural_network`

module.

Compare Stochastic learning strategies for MLPClassifier

Restricted Boltzmann Machine features for digit classification

Varying regularization in Multi-layer Perceptron

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 Mixed Types

Concatenating multiple feature extraction methods

Effect of transforming the targets in regression model

Pipelining: chaining a PCA and a logistic regression

Selecting dimensionality reduction with Pipeline and GridSearchCV

## Preprocessing¶

Examples concerning the `sklearn.preprocessing`

module.

Compare the effect of different scalers on data with outliers

Comparing Target Encoder with Other Encoders

Demonstrating the different strategies of KBinsDiscretizer

Map data to a normal distribution

Target Encoder’s Internal Cross fitting

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

Effect of varying threshold for self-training

Label Propagation digits active learning

Label Propagation digits: Demonstrating performance

Label Propagation learning a complex structure

Semi-supervised Classification on a Text Dataset

## Support Vector Machines¶

Examples concerning the `sklearn.svm`

module.

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

Plot classification boundaries with different SVM Kernels

Plot different SVM classifiers in the iris dataset

Plot the support vectors in LinearSVC

SVM-Anova: SVM with univariate feature selection

SVM: Maximum margin separating hyperplane

SVM: Separating hyperplane for unbalanced classes

Scaling the regularization parameter for SVCs

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

## Tutorial exercises¶

Exercises for the tutorials

Cross-validation on diabetes Dataset Exercise

Digits Classification Exercise

## Working with text documents¶

Examples concerning the `sklearn.feature_extraction.text`

module.

Classification of text documents using sparse features

Clustering text documents using k-means

FeatureHasher and DictVectorizer Comparison