Examples¶
Release Highlights¶
These examples illustrate the main features of the releases of scikit-learn.
Biclustering¶
Examples concerning biclustering techniques.
Biclustering documents with the Spectral Co-clustering algorithm
Calibration¶
Examples illustrating the calibration of predicted probabilities of classifiers.
Probability Calibration for 3-class classification
Classification¶
General examples about classification algorithms.
Linear and Quadratic Discriminant Analysis with covariance ellipsoid
Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification
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
Adjustment for chance in clustering performance evaluation
Agglomerative clustering with and without structure
Bisecting K-Means and Regular K-Means Performance Comparison
Comparing different clustering algorithms on toy datasets
Comparing different hierarchical linkage methods on toy datasets
Comparison of the K-Means and MiniBatchKMeans clustering algorithms
Empirical evaluation of the impact of k-means initialization
Hierarchical clustering: structured vs unstructured ward
Selecting the number of clusters with silhouette analysis on KMeans clustering
Various Agglomerative Clustering on a 2D embedding of digits
Covariance estimation¶
Examples concerning the sklearn.covariance module.
Robust covariance estimation and Mahalanobis distances relevance
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
Cross decomposition¶
Examples concerning the sklearn.cross_decomposition module.
Principal Component Regression vs Partial Least Squares Regression
Dataset examples¶
Examples concerning the sklearn.datasets module.
Decision Trees¶
Examples concerning the sklearn.tree module.
Plot the decision surface of decision trees trained on the iris dataset
Post pruning decision trees with cost complexity pruning
Decomposition¶
Examples concerning the sklearn.decomposition module.
Comparison of LDA and PCA 2D projection of Iris dataset
Factor Analysis (with rotation) to visualize patterns
Model selection with Probabilistic PCA and Factor Analysis (FA)
Developing Estimators¶
Examples concerning the development of Custom Estimator.
Ensemble methods¶
Examples concerning the sklearn.ensemble module.
Comparing Random Forests and Histogram Gradient Boosting models
Comparing random forests and the multi-output meta estimator
Hashing feature transformation using Totally Random Trees
Plot class probabilities calculated by the VotingClassifier
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
Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
Feature Selection¶
Examples concerning the sklearn.feature_selection module.
Recursive feature elimination with cross-validation
Gaussian Mixture Models¶
Examples concerning the sklearn.mixture module.
Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture
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
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.
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples
HuberRegressor vs Ridge on dataset with strong outliers
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 multinomial and One-vs-Rest Logistic Regression
Ridge coefficients as a function of the L2 Regularization
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.
Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…
t-SNE: The effect of various perplexity values on the shape
Miscellaneous¶
Miscellaneous and introductory examples for scikit-learn.
Comparing anomaly detection algorithms for outlier detection on toy datasets
Explicit feature map approximation for RBF kernels
The Johnson-Lindenstrauss bound for embedding with random projections
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
Multiclass Receiver Operating Characteristic (ROC)
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
Multioutput methods¶
Examples concerning the sklearn.multioutput module.
Nearest Neighbors¶
Examples concerning the sklearn.neighbors module.
Comparing Nearest Neighbors with and without Neighborhood Components Analysis
Dimensionality Reduction with Neighborhood Components Analysis
Neural Networks¶
Examples concerning the sklearn.neural_network module.
Compare Stochastic learning strategies for MLPClassifier
Restricted Boltzmann Machine features for digit classification
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
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
Demonstrating the different strategies of KBinsDiscretizer
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
Label Propagation digits: Demonstrating performance
Support Vector Machines¶
Examples concerning the sklearn.svm module.
Plot classification boundaries with different SVM Kernels
Plot different SVM classifiers in the iris dataset
Support Vector Regression (SVR) using linear and non-linear kernels
Tutorial exercises¶
Exercises for the tutorials
Working with text documents¶
Examples concerning the sklearn.feature_extraction.text module.
Classification of text documents using sparse features
