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7. Dataset transformations — scikit-learn 1.8.0...
scikit-learn provides a library of transformers, which may clean (see Preprocessing data), reduce (see Unsupervised dimensionality reduction), expand (see Kernel Approximation) or generate (see Fea...scikit-learn.org/stable/data_transforms.html -
Probability Calibration curves — scikit-learn 1...
When performing classification one often wants to predict not only the class label, but also the associated probability. This probability gives some kind of confidence on the prediction. This examp...scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html -
Gradient Boosting regularization — scikit-learn...
Illustration of the effect of different regularization strategies for Gradient Boosting. The example is taken from Hastie et al 2009 1. The loss function used is binomial deviance. Regularization v...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regularization.html -
Model Complexity Influence — scikit-learn 1.8.0...
Demonstrate how model complexity influences both prediction accuracy and computational performance. We will be using two datasets:,- Diabetes dataset for regression. This dataset consists of 10 mea...scikit-learn.org/stable/auto_examples/applications/plot_model_complexity_influence.html -
Wikipedia principal eigenvector — scikit-learn ...
A classical way to assert the relative importance of vertices in a graph is to compute the principal eigenvector of the adjacency matrix so as to assign to each vertex the values of the components ...scikit-learn.org/stable/auto_examples/applications/wikipedia_principal_eigenvector.html -
Release Highlights for scikit-learn 0.22 — scik...
We are pleased to announce the release of scikit-learn 0.22, which comes with many bug fixes and new features! We detail below a few of the major features of this release. For an exhaustive list of...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_22_0.html -
Release Highlights for scikit-learn 1.5 — sciki...
We are pleased to announce the release of scikit-learn 1.5! Many bug fixes and improvements were added, as well as some key new features. Below we detail the highlights of this release. For an exha...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html -
Comparing different clustering algorithms on to...
This example shows characteristics of different clustering algorithms on datasets that are “interesting” but still in 2D. With the exception of the last dataset, the parameters of each of these dat...scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html -
Various Agglomerative Clustering on a 2D embedd...
An illustration of various linkage option for agglomerative clustering on a 2D embedding of the digits dataset. The goal of this example is to show intuitively how the metrics behave, and not to fi...scikit-learn.org/stable/auto_examples/cluster/plot_digits_linkage.html -
Plot classification boundaries with different S...
This example shows how different kernels in a SVC(Support Vector Classifier) influence the classification boundaries in a binary, two-dimensional classification problem. SVCs aim to find a hyperpla...scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html