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scikit-learn 1.1.3
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A tutorial on statistical-learning for scientific data processing
A tutorial on statistical-learning for scientific data processing
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Statistical learning: the setting and the estimator object in scikit-learn
Datasets
Estimators objects
Supervised learning: predicting an output variable from high-dimensional observations
Nearest neighbor and the curse of dimensionality
Linear model: from regression to sparsity
Support vector machines (SVMs)
Model selection: choosing estimators and their parameters
Score, and cross-validated scores
Cross-validation generators
Grid-search and cross-validated estimators
Unsupervised learning: seeking representations of the data
Clustering: grouping observations together
Decompositions: from a signal to components and loadings
Putting it all together
Pipelining
Face recognition with eigenfaces
Open problem: Stock Market Structure