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6.3. Preprocessing data — scikit-learn 1.6.1 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 -
1.13. Feature selection — scikit-learn 1.6.1 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 -
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 1.6.1 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 -
7.1. Toy datasets — scikit-learn 1.6.1 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 -
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 -
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 -
Ordinary Least Squares and Ridge Regression Var...
Due to the few points in each dimension and the straight line that linear regression uses to follow these points as well as it can, noise on the observations will cause great variance as shown in t...scikit-learn.org/stable/auto_examples/linear_model/plot_ols_ridge_variance.html -
Effect of varying threshold for self-training —...
This example illustrates the effect of a varying threshold on self-training. The breast_cancer dataset is loaded, and labels are deleted such that only 50 out of 569 samples have labels. A SelfTrai...scikit-learn.org/stable/auto_examples/semi_supervised/plot_self_training_varying_threshold.html -
Ledoit-Wolf vs OAS estimation — scikit-learn 1....
The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and Wolf proposed a close formula to compute the asymptotically optimal shrinkage parameter (minimizing a...scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html