Sample pipeline for text feature extraction and evaluation
  • References/Python/scikit-learn/Examples/Model Selection

The dataset used in this example is the 20 newsgroups dataset which will be automatically downloaded and then cached and reused for

2025-01-10 15:47:30
Visualizing the stock market structure
  • References/Python/scikit-learn/Examples/Examples based on real world datasets

This example employs several unsupervised learning techniques to extract the stock market structure from variations in historical quotes. The quantity

2025-01-10 15:47:30
Gaussian Processes regression
  • References/Python/scikit-learn/Examples/Gaussian Process for Machine Learning

A simple one-dimensional regression example computed in two different ways: A noise-free case

2025-01-10 15:47:30
Logistic function
  • References/Python/scikit-learn/Examples/Generalized Linear Models

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.

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

This example shows the reconstruction of an image from a set of parallel projections, acquired along different angles. Such

2025-01-10 15:47:30
Comparison of kernel ridge regression and SVR
  • References/Python/scikit-learn/Examples/General examples

Both kernel ridge regression (KRR) and SVR learn a non-linear function by employing the kernel trick, i.e., they learn a linear function in the

2025-01-10 15:47:30
Classification of text documents
  • References/Python/scikit-learn/Examples/Working with text documents

This is an example showing how the scikit-learn can be used to classify documents by topics using a bag-of-words approach. This example

2025-01-10 15:47:30
Agglomerative clustering with different metrics
  • References/Python/scikit-learn/Examples/Clustering

Demonstrates the effect of different metrics on the hierarchical clustering. The example is engineered to show the effect of the choice

2025-01-10 15:47:30
Plot the decision boundaries of a VotingClassifier
  • References/Python/scikit-learn/Examples/Ensemble methods

Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset. Plot the class probabilities

2025-01-10 15:47:30
Confusion matrix
  • References/Python/scikit-learn/Examples/Model Selection

Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. The diagonal elements represent the number of points for which

2025-01-10 15:47:30