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HashingVectorizer — scikit-learn 1.5.2 document...
2) means unigrams and bigrams, and (2, 2) means only...n_features is large enough (e.g. 2 ** 18 for text classification...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html -
Model selection with Probabilistic PCA and Fact...
rand ( n_features ) + sigma / 2.0 X_hetero = X + rng . randn (...shrinkages = np . logspace ( - 2 , 0 , 30 ) cv = GridSearchCV (...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html -
Classifier comparison — scikit-learn 1.5.2 docu...
n_features = 2 , n_redundant = 0 , n_informative = 2 , random_state.... random . RandomState ( 2 ) X += 2 * rng . uniform ( size =...scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html -
oas — scikit-learn 1.5.2 documentation
formula (23) states that 2/p (p being the number of features)...because for a large p, the value of 2/p is so small that it doesn’t...scikit-learn.org/stable/modules/generated/oas-function.html -
BisectingKMeans — scikit-learn 1.5.2 documentation
2, 0, 2, 0, 2, 1, 1, 1], dtype=int32)...], ... [ 10 , 0 ], [ 2 , 1 ], [ 10 , 2 ], ... [ 10 , 8 ], [ 10...scikit-learn.org/stable/modules/generated/sklearn.cluster.BisectingKMeans.html -
KernelRidge — scikit-learn 1.5.2 documentation
float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\)...scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html -
PredictionErrorDisplay — scikit-learn 1.5.2 doc...
2 Release Highlights for scikit-learn 1.2 Combine predictors...Evaluation Guide . Added in version 1.2. Parameters : y_true ndarray of...scikit-learn.org/stable/modules/generated/sklearn.metrics.PredictionErrorDisplay.html -
RFE — scikit-learn 1.5.2 documentation
2, 5]) property classes_ # Classes...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html -
Feature discretization — scikit-learn 1.5.2 doc...
n_features = 2 , n_redundant = 0 , n_informative = 2 , random_state...GradientBoostingClas: 0.84 SVC: 0.84 dataset 2 --------- LogisticRegression:...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_classification.html -
Incremental PCA — scikit-learn 1.5.2 documentation
target n_components = 2 ipca = IncrementalPCA ( n_components...target_name in zip ( colors , [ 0 , 1 , 2 ], iris . target_names ): plt...scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html