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Release Highlights
Release Highlights for scikit-learn 1.9
Release Highlights for scikit-learn 1.8
Release Highlights for scikit-learn 1.7
Release Highlights for scikit-learn 1.6
Release Highlights for scikit-learn 1.5
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
A demo of the Spectral Biclustering algorithm
A demo of the Spectral Co-Clustering algorithm
Biclustering documents with the Spectral Co-clustering algorithm
Calibration
Comparison of Calibration of Classifiers
Probability Calibration curves
Probability Calibration for 3-class classification
Probability calibration of classifiers
Callbacks
Analysis of the convergence of penalized logistic regression models
Supporting callbacks in third party estimators
Classification
Classifier comparison
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
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 different metrics
An example of K-Means++ initialization
Bisecting K-Means and Regular K-Means Performance Comparison
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
Feature agglomeration vs. univariate selection
Hierarchical clustering with and without structure
Inductive Clustering
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
Vector Quantization Example
Covariance estimation
Ledoit-Wolf vs OAS estimation
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
Compare cross decomposition methods
Principal Component Regression vs Partial Least Squares Regression
Dataset examples
Plot randomly generated multilabel dataset
Decision Trees
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
Blind source separation using FastICA
Comparison of LDA and PCA 2D projection of Iris dataset
Faces dataset decompositions
Factor Analysis (with rotation) to visualize patterns
FastICA on 2D point clouds
Image denoising using dictionary learning
Incremental PCA
Kernel PCA
Model selection with Probabilistic PCA and Factor Analysis (FA)
Principal Component Analysis (PCA) on Iris Dataset
Sparse coding with a precomputed dictionary
Developing Estimators
__sklearn_is_fitted__
as Developer API
Ensemble methods
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
Features in Histogram Gradient Boosting Trees
Gradient Boosting Out-of-Bag estimates
Gradient Boosting regression
Gradient Boosting regularization
Hashing feature transformation using Totally Random Trees
IsolationForest example
Monotonic Constraints
Multi-class AdaBoosted Decision Trees
OOB Errors for Random Forests
Plot individual and voting regression predictions
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
Two-class AdaBoost
Visualizing the probabilistic predictions of a VotingClassifier
Examples based on real world datasets
Compressive sensing: tomography reconstruction with L1 prior (Lasso)
Faces recognition example using eigenfaces and kernel approximation
Image denoising using kernel PCA
Lagged features for time series forecasting
Model Complexity Influence
Out-of-core classification of text documents
Outlier detection on a real data set
Prediction Latency
Species distribution modeling
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
Comparison of F-test and mutual information
Model-based and sequential feature selection
Pipeline ANOVA SVM
Recursive feature elimination
Recursive feature elimination with cross-validation
Univariate Feature Selection
Frozen Estimators
Examples of Using
FrozenEstimator
Gaussian Mixture Models
Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture
Density Estimation for a Gaussian mixture
GMM Initialization Methods
GMM covariances
Gaussian Mixture Model Ellipsoids
Gaussian Mixture Model Selection
Gaussian Mixture Model Sine Curve
Gaussian Process for Machine Learning
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
Comparing Linear Bayesian Regressors
Curve Fitting with Bayesian Ridge Regression
Decision Boundaries of Multinomial and One-vs-Rest Logistic 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
Lasso, Lasso-LARS, and Elastic Net paths
MNIST classification using multinomial logistic + L1
Multiclass sparse logistic regression on 20newgroups
Non-negative least squares
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent
Ordinary Least Squares and Ridge Regression
Orthogonal Matching Pursuit
Plot Ridge coefficients as a function of the regularization
Plot multi-class SGD on the iris dataset
Poisson regression and non-normal loss
Polynomial and Spline interpolation
Quantile regression
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
SGD: Penalties
SGD: Weighted samples
SGD: convex loss functions
Theil-Sen Regression
Tweedie regression on insurance claims
Inspection
Advanced Plotting With Partial Dependence
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
Scalable learning with polynomial kernel approximation
Manifold learning
Comparison of Manifold Learning methods
Manifold Learning methods on a severed sphere
Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…
Multi-dimensional scaling
Swiss Roll And Swiss-Hole Reduction
t-SNE: The effect of various perplexity values on the shape
Miscellaneous
Comparing anomaly detection algorithms for outlier detection on toy datasets
Comparison of kernel ridge regression and SVR
