Gallery examples: FeatureHasher and DictVectorizer Comparison| scikit-learn
This example will demonstrate the set_output API to configure transformers to output pandas DataFrames. set_output can be configured per estimator by calling the set_output method or globally by se...| scikit-learn
Gallery examples: Scalable learning with polynomial kernel approximation Compare the effect of different scalers on data with outliers Clustering text documents using k-means| scikit-learn
Gallery examples: Hashing feature transformation using Totally Random Trees Manifold learning on handwritten digits: Locally Linear Embedding, Isomap… Clustering text documents using k-means| scikit-learn
Gallery examples: Column Transformer with Heterogeneous Data Sources FeatureHasher and DictVectorizer Comparison| scikit-learn
The dataset used in this example is The 20 newsgroups text dataset which will be automatically downloaded, cached and reused for the document classification example. In this example, we tune the hy...| scikit-learn
Gallery examples: Release Highlights for scikit-learn 1.4 Visualizing cross-validation behavior in scikit-learn| scikit-learn
Gallery examples: Plot classification probability Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression Multiclass sparse logistic regression on 20newgroups Multilabel classificati...| scikit-learn
This glossary hopes to definitively represent the tacit and explicit conventions applied in Scikit-learn and its API, while providing a reference for users and contributors. It aims to describe the...| scikit-learn
Gallery examples: Release Highlights for scikit-learn 1.5 Release Highlights for scikit-learn 1.4 Release Highlights for scikit-learn 1.2 Release Highlights for scikit-learn 1.1 Release Highlights ...| scikit-learn
Encode target labels with value between 0 and n_classes-1.| scikit-learn
The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream esti...| scikit-learn
Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would ha...| scikit-learn
I tried to illustrate the usage of scikit-learn pipelines.| kevinkle.in