Gallery examples: Species distribution modeling Principal Component Analysis (PCA) on Iris Dataset| scikit-learn
Gallery examples: Metadata Routing Displaying Pipelines Introducing the set_output API Post-tuning the decision threshold for cost-sensitive learning Target Encoder’s Internal Cross fitting Release...| scikit-learn
Gallery examples: Time-related feature engineering Plot classification probability Classifier comparison A demo of K-Means clustering on the handwritten digits data Principal Component Regression v...| scikit-learn
The PCA does an unsupervised dimensionality reduction, while the logistic regression does the prediction. We use a GridSearchCV to set the dimensionality of the PCA, Total running time of the scrip...| scikit-learn
This guide demonstrates how metadata can be routed and passed between objects in scikit-learn. If you are developing a scikit-learn compatible estimator or meta-estimator, you can check our related...| 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 1.4 Release Highlights for scikit-learn 0.24 Feature agglomeration vs. univariate selection Shrinkage covariance estimation: LedoitWolf vs OAS ...| scikit-learn
A small number of constants live in the built-in namespace. They are: Constants added by the site module: The site module (which is imported automatically during startup, except if the-S command-li...| Python documentation
The following sections describe the standard types that are built into the interpreter. The principal built-in types are numerics, sequences, mappings, classes, instances and exceptions. Some colle...| Python documentation