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Neural Networks — scikit-learn 1.7.1 documentation
Examples concerning the sklearn.neural_network module. Compare Stochastic learning strategies for MLPClassifier Restricted Boltzmann Machine features for digit classification Varying regularization...scikit-learn.org/stable/auto_examples/neural_networks/index.html -
is_regressor — scikit-learn 1.7.1 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version is_regressor # sklearn.base. is_regressor ( estimator...scikit-learn.org/stable/modules/generated/sklearn.base.is_regressor.html -
ConfusionMatrixDisplay — scikit-learn 1.7.1 doc...
performance Classification of text documents using sparse features Classification...Classification of text documents using sparse features On this page...scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html -
Plot classification boundaries with different S...
This example shows how different kernels in a SVC(Support Vector Classifier) influence the classification boundaries in a binary, two-dimensional classification problem. SVCs aim to find a hyperpla...scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html -
1.15. Isotonic regression — scikit-learn 1.7.1 ...
The class IsotonicRegression fits a non-decreasing real function to 1-dimensional data. It solves the following problem:\min \sum_i w_i (y_i - \hat{y}_i)^2 subject to\hat{y}_i \le \hat{y}_j wheneve...scikit-learn.org/stable/modules/isotonic.html -
Comparison of F-test and mutual information — s...
This example illustrates the differences between univariate F-test statistics and mutual information. We consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the target depends on t...scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html -
SGD: Maximum margin separating hyperplane — sci...
Plot the maximum margin separating hyperplane within a two-class separable dataset using a linear Support Vector Machines classifier trained using SGD. Total running time of the script:(0 minutes 0...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html -
7.7. Kernel Approximation — scikit-learn 1.7.1 ...
This submodule contains functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector machines (see Support Vector Machines). Th...scikit-learn.org/stable/modules/kernel_approximation.html -
11. Common pitfalls and recommended practices —...
The purpose of this chapter is to illustrate some common pitfalls and anti-patterns that occur when using scikit-learn. It provides examples of what not to do, along with a corresponding correct ex...scikit-learn.org/stable/common_pitfalls.html -
1.8. Cross decomposition — scikit-learn 1.7.1 d...
The cross decomposition module contains supervised estimators for dimensionality reduction and regression, belonging to the “Partial Least Squares” family. Cross decomposition algorithms find the f...scikit-learn.org/stable/modules/cross_decomposition.html