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randomized_range_finder — scikit-learn 1.5.2 do...
Skip to main content Back to top Ctrl + K GitHub randomized_range_finder # sklearn.utils.extmath. randomized_range_fi...scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.randomized_range_finder.html -
Recognizing hand-written digits — scikit-learn ...
This example shows how scikit-learn can be used to recognize images of hand-written digits, from 0-9. Digits dataset: The digits dataset consists of 8x8 pixel images of digits. The images attribute...scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html -
Early stopping of Stochastic Gradient Descent —...
Stochastic Gradient Descent is an optimization technique which minimizes a loss function in a stochastic fashion, performing a gradient descent step sample by sample. In particular, it is a very ef...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_early_stopping.html -
Permutation Importance with Multicollinear or C...
In this example, we compute the permutation_importance of the features to a trained RandomForestClassifier using the Breast cancer wisconsin (diagnostic) dataset. The model can easily get about 97%...scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance_multicollinear.html -
Plot Ridge coefficients as a function of the re...
Shows the effect of collinearity in the coefficients of an estimator. Ridge Regression is the estimator used in this example. Each color represents a different feature of the coefficient vector, an...scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_path.html -
Plot class probabilities calculated by the Voti...
Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier. First, three exemplary classifiers are initialized (...scikit-learn.org/stable/auto_examples/ensemble/plot_voting_probas.html -
sklearn.neural_network — scikit-learn 1.5.2 doc...
Models based on neural networks. User guide. See the Neural network models (supervised) and Neural network models (unsupervised) sections for further details.scikit-learn.org/stable/api/sklearn.neural_network.html -
Blind source separation using FastICA — scikit-...
An example of estimating sources from noisy data. Independent component analysis (ICA) is used to estimate sources given noisy measurements. Imagine 3 instruments playing simultaneously and 3 micro...scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html -
Image denoising using kernel PCA — scikit-learn...
This example shows how to use KernelPCA to denoise images. In short, we take advantage of the approximation function learned during fit to reconstruct the original image. We will compare the result...scikit-learn.org/stable/auto_examples/applications/plot_digits_denoising.html -
Train error vs Test error — scikit-learn 1.5.2 ...
Illustration of how the performance of an estimator on unseen data (test data) is not the same as the performance on training data. As the regularization increases the performance on train decrease...scikit-learn.org/stable/auto_examples/model_selection/plot_train_error_vs_test_error.html