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Developing Estimators — scikit-learn 1.7....
Examples concerning the development of Custom Estimator.__sklearn_is_fitted__ as Developer APIscikit-learn.org/stable/auto_examples/developing_estimators/index.html -
Incremental PCA — scikit-learn 1.7.2 docu...
Incremental principal component analysis (IPCA) is typically used as a replacement for principal component analysis (PCA) when the dataset to be decomposed is too large to fit in memory. IPCA build...scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html -
Logistic function — scikit-learn 1.7.2 do...
Shown in the plot is how the logistic regression would, in this synthetic dataset, classify values as either 0 or 1, i.e. class one or two, using the logistic curve. Total running time of the scrip...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic.html -
Kernel Approximation — scikit-learn 1.7.2...
Examples concerning the sklearn.kernel_approximation module. Scalable learning with polynomial kernel approximationscikit-learn.org/stable/auto_examples/kernel_approximation/index.html -
GMM covariances — scikit-learn 1.7.2 docu...
Demonstration of several covariances types for Gaussian mixture models. See Gaussian mixture models for more information on the estimator. Although GMM are often used for clustering, we can compare...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html -
1.13. Feature selection — scikit-learn 1....
The classes in the sklearn.feature_selection module can be used for feature selection/dimensionality reduction on sample sets, either to improve estimators’ accuracy scores or to boost their perfor...scikit-learn.org/stable/modules/feature_selection.html -
7.3. Preprocessing data — scikit-learn 1....
The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream esti...scikit-learn.org/stable/modules/preprocessing.html -
1.10. Decision Trees — scikit-learn 1.7.2...
Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning s...scikit-learn.org/stable/modules/tree.html -
1.16. Probability calibration — scikit-le...
When performing classification you often want not only to predict the class label, but also obtain a probability of the respective label. This probability gives you some kind of confidence on the p...scikit-learn.org/stable/modules/calibration.html -
Demonstrating the different strategies of KBins...
This example presents the different strategies implemented in KBinsDiscretizer: ‘uniform’: The discretization is uniform in each feature, which means that the bin widths are constant in each dimens...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_strategies.html