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sklearn.neural_network — scikit-learn 1.7.0 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 -
sklearn.model_selection — scikit-learn 1.7.0 do...
Tools for model selection, such as cross validation and hyper-parameter tuning. User guide. See the Cross-validation: evaluating estimator performance, Tuning the hyper-parameters of an estimator, ...scikit-learn.org/stable/api/sklearn.model_selection.html -
inplace_csr_row_normalize_l1 — scikit-learn 1.7...
Skip to main content Back to top Ctrl + K GitHub Choose version inplace_csr_row_normalize_l1 # sklearn.utils.sparsefu...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normaliz... -
Model selection with Probabilistic PCA and Fact...
Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the likelihood of new data can be used for model selection and covariance estimation. Here we compare PCA and...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html -
t-SNE: The effect of various perplexity values ...
An illustration of t-SNE on the two concentric circles and the S-curve datasets for different perplexity values. We observe a tendency towards clearer shapes as the perplexity value increases. The ...scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html -
Illustration of Gaussian process classification...
This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains consi...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html -
Compressive sensing: tomography reconstruction ...
This example shows the reconstruction of an image from a set of parallel projections, acquired along different angles. Such a dataset is acquired in computed tomography(CT). Without any prior infor...scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html -
7.9. Transforming the prediction target (y) — s...
Transforming the prediction target ( y): These are transformers that are not intended to be used on features, only on supervised learning targets. See also Transforming target in regression if you ...scikit-learn.org/stable/modules/preprocessing_targets.html -
Linear and Quadratic Discriminant Analysis with...
This example plots the covariance ellipsoids of each class and the decision boundary learned by LinearDiscriminantAnalysis(LDA) and QuadraticDiscriminantAnalysis(QDA). The ellipsoids display the do...scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html -
Multi-class AdaBoosted Decision Trees — scikit-...
This example shows how boosting can improve the prediction accuracy on a multi-label classification problem. It reproduces a similar experiment as depicted by Figure 1 in Zhu et al 1. The core prin...scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_multiclass.html