Prediction Latency
  • References/Python/scikit-learn/Examples/Examples based on real world datasets

This is an example showing the prediction latency of various scikit-learn estimators. The goal is to measure the latency one can expect when doing predictions either

2025-01-10 15:47:30
Gaussian process regression with noise-level estimation
  • References/Python/scikit-learn/Examples/Gaussian Process for Machine Learning

This example illustrates that GPR with a sum-kernel including a WhiteKernel can estimate the noise level of data. An illustration

2025-01-10 15:47:30
Shrinkage covariance estimation
  • References/Python/scikit-learn/Examples/Covariance estimation

When working with covariance estimation, the usual approach is to use a maximum likelihood estimator, such as the

2025-01-10 15:47:30
Two-class AdaBoost
  • References/Python/scikit-learn/Examples/Ensemble methods

This example fits an AdaBoosted decision stump on a non-linearly separable classification dataset composed of two ?Gaussian quantiles? clusters (see

2025-01-10 15:47:30
Compare Stochastic learning strategies for MLPClassifier
  • References/Python/scikit-learn/Examples/Neural Networks

This example visualizes some training loss curves for different stochastic learning strategies, including SGD and Adam. Because of time-constraints

2025-01-10 15:47:30
Plot class probabilities calculated by the VotingClassifier
  • References/Python/scikit-learn/Examples/Ensemble methods

Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the

2025-01-10 15:47:30
Comparison of F-test and mutual information
  • References/Python/scikit-learn/Examples/Feature Selection

This example illustrates the differences between univariate F-test statistics and mutual information. We consider 3 features x_1, x_2, x_3

2025-01-10 15:47:30
Species distribution modeling
  • References/Python/scikit-learn/Examples/Examples based on real world datasets

Modeling species? geographic distributions is an important problem in conservation biology. In this example we model the geographic distribution of two south american

2025-01-10 15:47:30
A demo of K-Means clustering on the handwritten digits data
  • References/Python/scikit-learn/Examples/Clustering

In this example we compare the various initialization strategies for K-means in terms of runtime and quality of the results.

2025-01-10 15:47:30
Multi-dimensional scaling
  • References/Python/scikit-learn/Examples/Manifold learning

An illustration of the metric and non-metric MDS on generated noisy data. The reconstructed points using the metric MDS and non metric MDS are slightly shifted

2025-01-10 15:47:30