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1.8. Cross decomposition — scikit-learn 1.7.1 d...
follows: Set \(X_1\) to \(X\) and \(Y_1\) to \(Y\) . Then, for each...\(\text{Cov}(X_k u_k, Y_k v_k)\) . b) Project \(X_k\) and \(Y_k\) on the singular...scikit-learn.org/stable/modules/cross_decomposition.html -
2.1. Gaussian mixture models — scikit-learn 1.7...
finite mixture. 2.1.2.1. The Dirichlet Process # Here we describe...detailed below. 2.1.1. Gaussian Mixture # The GaussianMixture object...scikit-learn.org/stable/modules/mixture.html -
completeness_score — scikit-learn 1.7.1 documen...
completeness_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Non-perfect...completeness_score ([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 0 , 1 ])) 0.0 >>> print...scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html -
roadmap.rst.txt
issue tracker. Architectural / general goals ---------- The list is...topic. Statement of purpose: Scikit-learn in 2018 ---------- Eleven...scikit-learn.org/stable/_sources/roadmap.rst.txt -
ExtraTreesRegressor — scikit-learn 1.7.1 docume...
“absolute_error”, “friedman_mse”, “poisson”}, default=”squared_error” The...changed from 10 to 100 in 0.22. criterion {“squared_error”, “absolute_error”,...scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html -
Isomap — scikit-learn 1.7.1 documentation
manifold. eigen_solver {‘auto’, ‘arpack’, ‘dense’}, default=’auto’...algorithm. neighbors_algorithm {‘auto’, ‘brute’, ‘kd_tree’, ‘ball_tree’},...scikit-learn.org/stable/modules/generated/sklearn.manifold.Isomap.html -
plot_kmeans_digits.ipynb
{n_features}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source":...{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source":...scikit-learn.org/stable/_downloads/6bf322ce1724c13e6e0f8f719ebd253c/plot_kmeans_digits.ipynb -
train_test_split — scikit-learn 1.7.1 documenta...
2 1 4.9 3.0 1.4 0.2 2 4.7 3.2 1.3 0.2 3 4.6 3.1 1.5 0.2 4 5.0...5.0 3.6 1.4 0.2 >>> y . head () 0 0 1 0 2 0 3 0 4 0 ... >>> X_train...scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html -
AffinityPropagation — scikit-learn 1.7.1 docume...
= np . array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2 ],...1, 1, 1]) >>> clustering . predict ([[ 0 , 0 ], [ 4 , 4 ]]) array([0,...scikit-learn.org/stable/modules/generated/sklearn.cluster.AffinityPropagation.html -
Birch — scikit-learn 1.7.1 documentation
- 0.3 , 1 ], [ 0 , - 1 ], [ 0.3 , - 1 ], [ - 0.3 , - 1 ]] >>>...Birch(n_clusters=None) >>> brc . predict ( X ) array([0, 0, 0, 1, 1, 1]) For a...scikit-learn.org/stable/modules/generated/sklearn.cluster.Birch.html