Examples

General examples

General-purpose and introductory examples for the scikit.

Plotting Cross-Validated Predictions

Concatenating multiple feature extraction methods

Isotonic Regression

Imputing missing values before building an estimator

Pipelining: chaining a PCA and a logistic regression

Multilabel classification

Face completion with a multi-output estimators

The Johnson-Lindenstrauss bound for embedding with random projections

Comparison of kernel ridge regression and SVR

Feature Union with Heterogeneous Data Sources

Explicit feature map approximation for RBF kernels

Examples based on real world datasets

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

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

Outlier detection on a real data set

Compressive sensing: tomography reconstruction with L1 prior (Lasso)

Faces recognition example using eigenfaces and SVMs

Model Complexity Influence

Species distribution modeling

Visualizing the stock market structure

Wikipedia principal eigenvector

Prediction Latency

Libsvm GUI

Out-of-core classification of text documents

Biclustering

Examples concerning the sklearn.cluster.bicluster module.

A demo of the Spectral Co-Clustering algorithm

A demo of the Spectral Biclustering 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 of classifiers

Probability Calibration for 3-class classification

Classification

General examples about classification algorithms.

Recognizing hand-written digits

Normal and Shrinkage Linear Discriminant Analysis for classification

Plot classification probability

Classifier comparison

Linear and Quadratic Discriminant Analysis with confidence ellipsoid

Clustering

Examples concerning the sklearn.cluster module.

A demo of the mean-shift clustering algorithm

Feature agglomeration

Demo of affinity propagation clustering algorithm

Demonstration of k-means assumptions

Agglomerative clustering with and without structure

Demo of DBSCAN clustering algorithm

Online learning of a dictionary of parts of faces

A demo of structured Ward hierarchical clustering on a raccoon face image

Segmenting the picture of a raccoon face in regions

Hierarchical clustering: structured vs unstructured ward

Spectral clustering for image segmentation

K-means Clustering

Various Agglomerative Clustering on a 2D embedding of digits

Vector Quantization Example

Color Quantization using K-Means

Compare BIRCH and MiniBatchKMeans

Feature agglomeration vs. univariate selection

Agglomerative clustering with different metrics

Empirical evaluation of the impact of k-means initialization

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

Adjustment for chance in clustering performance evaluation

Comparing different clustering algorithms on toy datasets

Comparison of the K-Means and MiniBatchKMeans clustering algorithms

Selecting the number of clusters with silhouette analysis on KMeans clustering

Covariance estimation

Examples concerning the sklearn.covariance module.

Ledoit-Wolf vs OAS estimation

Outlier detection with several methods.

Sparse inverse covariance estimation

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood

Robust covariance estimation and Mahalanobis distances relevance

Robust vs Empirical covariance estimate

Cross decomposition

Examples concerning the sklearn.cross_decomposition module.

Compare cross decomposition methods

Dataset examples

Examples concerning the sklearn.datasets module.

The Digit Dataset

Plot randomly generated classification dataset

The Iris Dataset

Plot randomly generated multilabel dataset

Decomposition

Examples concerning the sklearn.decomposition module.

Comparison of LDA and PCA 2D projection of Iris dataset

Incremental PCA

PCA example with Iris Data-set

Blind source separation using FastICA

Kernel PCA

FastICA on 2D point clouds

Sparse coding with a precomputed dictionary

Principal components analysis (PCA)

Model selection with Probabilistic PCA and Factor Analysis (FA)

Faces dataset decompositions

Image denoising using dictionary learning

Ensemble methods

Examples concerning the sklearn.ensemble module.

Pixel importances with a parallel forest of trees

Decision Tree Regression with AdaBoost

Feature importances with forests of trees

Plot the decision boundaries of a VotingClassifier

Plot class probabilities calculated by the VotingClassifier

Gradient Boosting regularization

OOB Errors for Random Forests

Gradient Boosting regression

Prediction Intervals for Gradient Boosting Regression

Partial Dependence Plots

Hashing feature transformation using Totally Random Trees

Two-class AdaBoost

Discrete versus Real AdaBoost

Multi-class AdaBoosted Decision Trees

Feature transformations with ensembles of trees

Gradient Boosting Out-of-Bag estimates

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

Single estimator versus bagging: bias-variance decomposition

Tutorial exercises

Exercises for the tutorials

Digits Classification Exercise

Cross-validation on Digits Dataset Exercise

SVM Exercise

Cross-validation on diabetes Dataset Exercise

Feature Selection

Examples concerning the sklearn.feature_selection module.

Pipeline Anova SVM

Recursive feature elimination

Recursive feature elimination with cross-validation

Feature selection using SelectFromModel and LassoCV

Test with permutations the significance of a classification score

Univariate Feature Selection

Gaussian Process for Machine Learning

Examples concerning the sklearn.gaussian_process module.

Gaussian Processes regression: goodness-of-fit on the ‘diabetes’ dataset

Gaussian Processes classification example: exploiting the probabilistic output

Gaussian Processes regression: basic introductory example

Generalized Linear Models

Examples concerning the sklearn.linear_model module.

Lasso path using LARS

SGD: convex loss functions

SGD: Maximum margin separating hyperplane

SGD: Weighted samples

Plot Ridge coefficients as a function of the regularization

Robust linear model estimation using RANSAC

Polynomial interpolation

Logistic Regression 3-class Classifier

Path with L1- Logistic Regression

Linear Regression Example

Ordinary Least Squares and Ridge Regression Variance

Comparing various online solvers

Logit function

Joint feature selection with multi-task Lasso

SGD: Penalties

Lasso on dense and sparse data

Lasso and Elastic Net for Sparse Signals

Bayesian Ridge Regression

Sparsity Example: Fitting only features 1 and 2

Robust linear estimator fitting

Automatic Relevance Determination Regression (ARD)

L1 Penalty and Sparsity in Logistic Regression

Plot multi-class SGD on the iris dataset

Theil-Sen Regression

Orthogonal Matching Pursuit

Lasso and Elastic Net

Lasso model selection: Cross-Validation / AIC / BIC

Sparse recovery: feature selection for sparse linear models

Manifold learning

Examples concerning the sklearn.manifold module.

Swiss Roll reduction with LLE

Multi-dimensional scaling

Comparison of Manifold Learning methods

Manifold Learning methods on a severed sphere

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

Gaussian Mixture Models

Examples concerning the sklearn.mixture module.

Density Estimation for a mixture of Gaussians

Gaussian Mixture Model Ellipsoids

Gaussian Mixture Model Sine Curve

Gaussian Mixture Model Selection

GMM classification

Model Selection

Examples concerning model selection, mostly contained in the sklearn.grid_search and sklearn.cross_validation modules.

Plotting Validation Curves

Underfitting vs. Overfitting

Confusion matrix

Receiver Operating Characteristic (ROC) with cross validation

Parameter estimation using grid search with cross-validation

Train error vs Test error

Comparing randomized search and grid search for hyperparameter estimation

Precision-Recall

Sample pipeline for text feature extraction and evaluation

Plotting Learning Curves

Receiver Operating Characteristic (ROC)

Nearest Neighbors

Examples concerning the sklearn.neighbors module.

Nearest Neighbors regression

Nearest Neighbors Classification

Nearest Centroid Classification

Kernel Density Estimation

Kernel Density Estimate of Species Distributions

Hyper-parameters of Approximate Nearest Neighbors

Simple 1D Kernel Density Estimation

Scalability of Approximate Nearest Neighbors

Neural Networks

Examples concerning the sklearn.neural_network module.

Restricted Boltzmann Machine features for digit classification

Preprocessing

Examples concerning the sklearn.preprocessing module.

Using FunctionTransformer to select columns

Robust Scaling on Toy Data

Semi Supervised Classification

Examples concerning the sklearn.semi_supervised module.

Label Propagation learning a complex structure

Decision boundary of label propagation versus SVM on the Iris dataset

Label Propagation digits: Demonstrating performance

Label Propagation digits active learning

Support Vector Machines

Examples concerning the sklearn.svm module.

Non-linear SVM

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

SVM: Maximum margin separating hyperplane

SVM: Separating hyperplane for unbalanced classes

SVM with custom kernel

SVM-Anova: SVM with univariate feature selection

SVM: Weighted samples

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

Plot different SVM classifiers in the iris dataset

SVM-Kernels

SVM Margins Example

Scaling the regularization parameter for SVCs

RBF SVM parameters

Working with text documents

Examples concerning the sklearn.feature_extraction.text module.

FeatureHasher and DictVectorizer Comparison

Classification of text documents: using a MLComp dataset

Clustering text documents using k-means

Classification of text documents using sparse features

Decision Trees

Examples concerning the sklearn.tree module.

Decision Tree Regression

Multi-output Decision Tree Regression

Plot the decision surface of a decision tree on the iris dataset