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Missing Value Imputation — scikit-learn 1...
Examples concerning the sklearn.impute module. Imputing missing values before building an estimator Imputing missing values with variants of IterativeImputerscikit-learn.org/stable/auto_examples/impute/index.html -
Species distribution modeling — scikit-le...
Modeling species’ geographic distributions is an important problem in conservation biology. In this example, we model the geographic distribution of two South American mammals given past observatio...scikit-learn.org/stable/auto_examples/applications/plot_species_distribution_modeling.html -
Multi-dimensional scaling — scikit-learn ...
An illustration of the metric and non-metric MDS on generated noisy data. Dataset preparation: We start by uniformly generating 20 points in a 2D space. Now we compute pairwise distances between al...scikit-learn.org/stable/auto_examples/manifold/plot_mds.html -
Kernel Density Estimation — scikit-learn ...
This example shows how kernel density estimation (KDE), a powerful non-parametric density estimation technique, can be used to learn a generative model for a dataset. With this generative model in ...scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html -
SVM with custom kernel — scikit-learn 1.7...
Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors. Total running time of the script:(0 minutes 0.095 seconds) Launch binder Lau...scikit-learn.org/stable/auto_examples/svm/plot_custom_kernel.html -
Support Vector Machines — scikit-learn 1....
Examples concerning the sklearn.svm module. One-class SVM with non-linear kernel (RBF) Plot classification boundaries with different SVM Kernels Plot different SVM classifiers in the iris dataset P...scikit-learn.org/stable/auto_examples/svm/index.html -
Version 0.17 — scikit-learn 1.7.2 documen...
Version 0.17.1: February 18, 2016 Changelog: Bug fixes: Upgrade vendored joblib to version 0.9.4 that fixes an important bug in joblib.Parallel that can silently yield to wrong results when working...scikit-learn.org/stable/whats_new/v0.17.html -
1.7. Gaussian Processes — scikit-learn 1....
Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems. The advantages of Gaussian processes are: The prediction i...scikit-learn.org/stable/modules/gaussian_process.html -
2.6. Covariance estimation — scikit-learn...
Many statistical problems require the estimation of a population’s covariance matrix, which can be seen as an estimation of data set scatter plot shape. Most of the time, such an estimation has to ...scikit-learn.org/stable/modules/covariance.html -
7.6. Random Projection — scikit-learn 1.7...
The sklearn.random_projection module implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional varianc...scikit-learn.org/stable/modules/random_projection.html