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Imputing missing values before building an esti...
Missing values can be replaced by the mean, the median or the most frequent value using the basic SimpleImputer. In this example we will investigate different imputation techniques: imputation by t...scikit-learn.org/stable/auto_examples/impute/plot_missing_values.html -
1.10. Decision Trees — scikit-learn 1.5.2 docum...
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-learn 1....
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 -
2.8. Density Estimation — scikit-learn 1.5.2 do...
Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation techniques are mixture models such as...scikit-learn.org/stable/modules/density.html -
Release History — scikit-learn 1.5.2 documentation
Changelogs and release notes for all scikit-learn releases are linked in this page. Version 1.5- Version 1.5.2, Version 1.5.1, Version 1.5.0., Version 1.4- Version 1.4.2, Version 1.4.1, Version 1.4...scikit-learn.org/stable/whats_new.html -
Developing Estimators — scikit-learn 1.5.2 docu...
Examples concerning the development of Custom Estimator.__sklearn_is_fitted__ as Developer APIscikit-learn.org/stable/auto_examples/developing_estimators/index.html -
Logistic function — scikit-learn 1.5.2 document...
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 1.5.2 docum...
Examples concerning the sklearn.kernel_approximation module. Scalable learning with polynomial kernel approximationscikit-learn.org/stable/auto_examples/kernel_approximation/index.html -
Feature discretization — scikit-learn 1.5.2 doc...
A demonstration of feature discretization on synthetic classification datasets. Feature discretization decomposes each feature into a set of bins, here equally distributed in width. The discrete va...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_classification.html -
Incremental PCA — scikit-learn 1.5.2 documentation
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