Using FunctionTransformer to select columns
  • References/Python/scikit-learn/Examples/Preprocessing

Shows how to use a function transformer in a pipeline. If you know your dataset?s first principle component is irrelevant for a classification task

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Adjustment for chance in clustering performance evaluation
  • References/Python/scikit-learn/Examples/Clustering

The following plots demonstrate the impact of the number of clusters and number of samples on various clustering performance evaluation

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Plot multinomial and One-vs-Rest Logistic Regression
  • References/Python/scikit-learn/Examples/Generalized Linear Models

Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers

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Theil-Sen Regression
  • References/Python/scikit-learn/Examples/Generalized Linear Models

Computes a Theil-Sen Regression on a synthetic dataset. See

2025-01-10 15:47:30
Receiver Operating Characteristic
  • References/Python/scikit-learn/Examples/Model Selection

Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality. ROC curves typically feature true positive rate

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Feature agglomeration
  • References/Python/scikit-learn/Examples/Clustering

These images how similar features are merged together using feature agglomeration.

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Decision Tree Regression with AdaBoost
  • References/Python/scikit-learn/Examples/Ensemble methods

A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D sinusoidal dataset with a small amount of Gaussian noise. 299 boosts (300 decision

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Simple 1D Kernel Density Estimation
  • References/Python/scikit-learn/Examples/Nearest Neighbors

This example uses the

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Image denoising using dictionary learning
  • References/Python/scikit-learn/Examples/Decomposition

An example comparing the effect of reconstructing noisy fragments of a raccoon face image using firstly online

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Sparse recovery
  • References/Python/scikit-learn/Examples/Generalized Linear Models

Given a small number of observations, we want to recover which features of X are relevant to explain y. For this

2025-01-10 15:47:30