Scikit-learn Examples

“Scikit-learn Examples” is a valuable resource that provides practical demonstrations and implementations of machine learning algorithms using the scikit-learn library in Python. The content covers a wide range of topics, from data preprocessing to model evaluation, making it suitable for both beginners and experienced practitioners in the field of machine learning. By leveraging real-world examples and code snippets, this source aims to enhance understanding and proficiency in utilizing scikit-learn for various machine learning tasks. Whether you are looking to explore classification, regression, clustering, or other machine learning techniques, “Scikit-learn Examples” offers insightful guidance and hands-on experience.

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Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.22 We are pleased to announce the release of scikit-learn 0.22, which comes with many bug fixes and new features! We detail below a few of the major features of t...

A demo of the Spectral Biclustering algorithm

Scikit-learn Examples

A demo of the Spectral Biclustering algorithm This example demonstrates how to generate a checkerboard dataset and bicluster it using the Spectral Biclustering algorithm. The data is generated with th...

Pixel importances with a parallel forest of trees

Scikit-learn Examples

Pixel importances with a parallel forest of trees This example shows the use of a forest of trees to evaluate the impurity based importance of the pixels in an image classification task on the faces d...

Out-of-core classification of text documents

Scikit-learn Examples

Out-of-core classification of text documents This is an example showing how scikit-learn can be used for classification using an out-of-core approach: learning from data that doesn’t fit into main mem...

Gaussian Processes regression: basic introductory example

Scikit-learn Examples

Gaussian Processes regression: basic introductory example A simple one-dimensional regression example computed in two different ways: A noise-free case A noisy case with known noise-level per datapoin...

Early stopping of Stochastic Gradient Descent

Scikit-learn Examples

Early stopping of Stochastic Gradient Descent Stochastic Gradient Descent is an optimization technique which minimizes a loss function in a stochastic fashion, performing a gradient descent step sampl...

Plot Ridge coefficients as a function of the L2 regularization

Scikit-learn Examples

Plot Ridge coefficients as a function of the L2 regularization Ridge Regression is the estimator used in this example. Each color in the left plot represents one different dimension of the coefficient...

Poisson regression and non-normal loss

Scikit-learn Examples

Poisson regression and non-normal loss This example illustrates the use of log-linear Poisson regression on the French Motor Third-Party Liability Claims dataset from 1 and compares it with a linear m...

Isotonic Regression

Scikit-learn Examples

Isotonic Regression An illustration of the isotonic regression on generated data (non-linear monotonic trend with homoscedastic uniform noise). The isotonic regression algorithm finds a non-decreasing...

Train error vs Test error

Scikit-learn Examples

Train error vs Test error Illustration of how the performance of an estimator on unseen data (test data) is not the same as the performance on training data. As the regularization increases the perfor...

Dimensionality Reduction with Neighborhood Components Analysis

Scikit-learn Examples

Dimensionality Reduction with Neighborhood Components Analysis Sample usage of Neighborhood Components Analysis for dimensionality reduction. This example compares different (linear) dimensionality re...

Column Transformer with Mixed Types

Scikit-learn Examples

Column Transformer with Mixed Types This example illustrates how to apply different preprocessing and feature extraction pipelines to different subsets of features, using ColumnTransformer . This is p...