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BaseEstimator — scikit-learn 1.7.1 documentation
param = 1 ): ... self . param = param ... def fit...self , X , y = None ): ... self . is_fitted_ = True ... return...scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html -
Scalable learning with polynomial kernel approx...
y = fetch_covtype ( return_X_y = True ) y [ y != 2 ] = 0 y...SVC ksvm = SVC ( C = 500.0 , kernel = "poly" , degree = 4 , coef0...scikit-learn.org/stable/auto_examples/kernel_approximation/plot_scalable_poly_kernels.html -
Target Encoder’s Internal Cross fitting — sciki...
transform_output = "pandas" ) ridge = Ridge ( alpha = 1e-6 , solver = "lsqr"...n_samples = 50_000 rng = np . random . RandomState ( 42 ) y = rng ....scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder_cross_val.html -
Single estimator versus bagging: bias-variance ...
* 10 - 5 X = np . sort ( X ) if n_repeat == 1 : y = f ( X ) +...X_train = [] y_train = [] for i in range ( n_repeat ): X , y = generate...scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html -
L1 Penalty and Sparsity in Logistic Regression ...
clf_l1_LR = LogisticRegression ( C = C , penalty = "l1" , tol = 0.01...penalty = "l2" , tol = 0.01 , solver = "saga" ) clf_en_LR = LogisticRegression...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html -
Principal Component Regression vs Partial Least...
mean = [ 0 , 0 ], cov = cov , size = n_samples ) pca = PCA ( n_components...label = f "Component { i } " , linewidth = 5 , color = f "C {...scikit-learn.org/stable/auto_examples/cross_decomposition/plot_pcr_vs_pls.html -
GroupShuffleSplit — scikit-learn 1.7.1 document...
splits=2, random_state=42, test_size=None, train_size=0.7) >>>...n_splits = 5 , * , test_size = None , train_size = None , random_state...scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupShuffleSplit.html -
d2_pinball_score — scikit-learn 1.7.1 documenta...
sample_weight = None , alpha = 0.5 , multioutput = 'uniform_average'...(n_samples,), default=None Sample weights. alpha float, default=0.5 Slope...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_pinball_score.html -
export_graphviz — scikit-learn 1.7.1 documentation
out_file = None , * , max_depth = None , feature_names = None ,..., class_names = None , label = 'all' , filled = False , leaves_parallel...scikit-learn.org/stable/modules/generated/sklearn.tree.export_graphviz.html -
SGD: Weighted samples — scikit-learn 1.7.1 docu...
c = y , s = sample_weight , alpha = 0.9 , cmap = plt . cm...alpha = 0.01 , max_iter = 100 ) clf . fit ( X , y ) Z = clf ....scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html