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Fitting an Elastic Net with a precomputed Gram ...
see the documentation for the sample_weight parameter...nbviewer.org. ElasticNet ? Documentation for ElasticNet i Fitted...scikit-learn.org/stable/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_... -
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 -
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 -
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 -
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 -
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 -
9. Computing with scikit-learn — scikit-learn 1...
Strategies to scale computationally: bigger data- Scaling with instances using out-of-core learning., Computational Performance- Prediction Latency, Prediction Throughput, Tips and Tricks., Paralle...scikit-learn.org/stable/computing.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 -
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 -
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