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 1.4 Release Highlights for scikit-learn 0.24 Feature agglomeration vs. univariate selection Shrinkage covariance estimation: LedoitWolf vs OAS ...| scikit-learn
Gallery examples: Release Highlights for scikit-learn 1.3 Model selection with Probabilistic PCA and Factor Analysis (FA) Lagged features for time series forecasting Imputing missing values before ...| 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
Gallery examples: L1-based models for Sparse Signals Linear Regression Example Non-negative least squares Failure of Machine Learning to infer causal effects Effect of transforming the targets in r...| scikit-learn