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Plot randomly generated classification dataset ...
c = Y1 , s = 25 , edgecolor = "k"...], X1 [:, 1 ], marker = "o" , c = Y1 , s = 25 , edgecolor = "k"...scikit-learn.org/stable/auto_examples/datasets/plot_random_dataset.html -
Two-class AdaBoost — scikit-learn 1.4.2 documen...
c = c , cmap = plt . cm . Paired ,...for colormapping provided via 'c'. Parameters 'cmap' will be ignored...scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_twoclass.html -
Comparing random forests and the multi-output m...
c = "c" , s = s , marker = "^" , alpha...y_test [:, 1 ], edgecolor = "k" , c = "navy" , s = s , marker = "s"...scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html -
Gaussian processes on discrete data structures ...
0 if c else - 1.0 for c in Y_train ], s = 100...( X_test )), [ 1.0 if c else - 1.0 for c in Y_test ], s = 100...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_on_structured_data.html -
4.1. Partial Dependence and Individual Conditio...
\mathbb{E}_{X_C}\left[ f(x_S, X_C) \right]\\ &= \int f(x_S, x_C) p(x_C)...p(x_C) dx_C,\end{split}\] where \(f(x_S, x_C)\) is the response function...scikit-learn.org/stable/modules/partial_dependence.html -
SVM Tie Breaking Example — scikit-learn 1.4.2 d...
C = 1 , break_ties = break_ties...) pred = svm . predict ( np . c_ [ xx . ravel (), yy . ravel ()])...scikit-learn.org/stable/auto_examples/svm/plot_svm_tie_breaking.html -
sklearn.metrics.confusion_matrix — scikit-learn...
a confusion matrix \(C\) is such that \(C_{i, j}\) is equal to...negatives is \(C_{0,0}\) , false negatives is \(C_{1,0}\) , true...scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html -
Importance of Feature Scaling — scikit-learn 1....
"Optimal C for the unscaled PCA: { unscaled_clf [ - 1 ] . C_ [ 0...scaled_clf [ - 1 ] . C_ [ 0 ] : .2f } " ) Optimal C for the unscaled...scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html -
Supervised learning: predicting an output varia...
c_ [ .5 , 1 ] . T >>> y = [ .5 , 1 ] >>> test = np . c_ [...is set by the C parameter: a small value for C means the margin...scikit-learn.org/stable/tutorial/statistical_inference/supervised_learning.html -
Feature discretization — scikit-learn 1.4.2 doc...
{ "logisticregression__C" : np . logspace ( - 1 , 1 , 3...dual = "auto" )), { "linearsvc__C" : np . logspace ( - 1 , 1 , 3...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_classification.html