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Imputing missing values before building an esti...
Missing values can be replaced by the mean, the median or the most frequent value using the basic SimpleImputer. In this example we will investigate different imputation techniques: imputation by t...scikit-learn.org/stable/auto_examples/impute/plot_missing_values.html -
1.13. Feature selection — scikit-learn 1.7.2 do...
The classes in the sklearn.feature_selection module can be used for feature selection/dimensionality reduction on sample sets, either to improve estimators’ accuracy scores or to boost their perfor...scikit-learn.org/stable/modules/feature_selection.html -
2.8. Density Estimation — scikit-learn 1.7.2 do...
Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation techniques are mixture models such as...scikit-learn.org/stable/modules/density.html -
7.3. Preprocessing data — scikit-learn 1.7.2 do...
The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream esti...scikit-learn.org/stable/modules/preprocessing.html -
1.16. Probability calibration — scikit-learn 1....
When performing classification you often want not only to predict the class label, but also obtain a probability of the respective label. This probability gives you some kind of confidence on the p...scikit-learn.org/stable/modules/calibration.html -
1.10. Decision Trees — scikit-learn 1.7.2 docum...
Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning s...scikit-learn.org/stable/modules/tree.html -
8.1. Toy datasets — scikit-learn 1.7.2 document...
scikit-learn comes with a few small standard datasets that do not require to download any file from some external website. They can be loaded using the following functions: These datasets are usefu...scikit-learn.org/stable/datasets/toy_dataset.html -
L1-based models for Sparse Signals — scikit-lea...
The present example compares three l1-based regression models on a synthetic signal obtained from sparse and correlated features that are further corrupted with additive gaussian noise: a Lasso;, a...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.html -
Model-based and sequential feature selection — ...
This example illustrates and compares two approaches for feature selection: SelectFromModel which is based on feature importance, and SequentialFeatureSelector which relies on a greedy approach. We...scikit-learn.org/stable/auto_examples/feature_selection/plot_select_from_model_diabetes.html -
A demo of the mean-shift clustering algorithm —...
Reference: Dorin Comaniciu and Peter Meer, “Mean Shift: A robust approach toward feature space analysis”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. pp. 603-619. Generate...scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html