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Decision boundary of semi-supervised classifier...
A comparison for the decision boundaries generated on the iris dataset by Label Spreading, Self-training and SVM. This example demonstrates that Label Spreading and Self-training can learn good bou...scikit-learn.org/stable/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html -
Decision Boundaries of Multinomial and One-vs-R...
This example compares decision boundaries of multinomial and one-vs-rest logistic regression on a 2D dataset with three classes. We make a comparison of the decision boundaries of both methods that...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_multinomial.html -
2.9. Neural network models (unsupervised) — sci...
Restricted Boltzmann machines: Restricted Boltzmann machines (RBM) are unsupervised nonlinear feature learners based on a probabilistic model. The features extracted by an RBM or a hierarchy of RBM...scikit-learn.org/stable/modules/neural_networks_unsupervised.html -
Lasso model selection: AIC-BIC / cross-validati...
This example focuses on model selection for Lasso models that are linear models with an L1 penalty for regression problems. Indeed, several strategies can be used to select the value of the regular...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_model_selection.html -
3.5. Validation curves: plotting scores to eval...
Every estimator has its advantages and drawbacks. Its generalization error can be decomposed in terms of bias, variance and noise. The bias of an estimator is its average error for different traini...scikit-learn.org/stable/modules/learning_curve.html -
3.1. Cross-validation: evaluating estimator per...
Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would ha...scikit-learn.org/stable/modules/cross_validation.html -
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