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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 -
1.2. Linear and Quadratic Discriminant Analysis...
\mathcal{R}^d\) : \[P(y=k | x) = \frac{P(x | y=k) P(y=k)}{P(x)} = \frac{P(x...P(y=k | x) &= \log P(x | y=k) + \log P(y = k) + Cst \\ &= -\frac{1}{2}...scikit-learn.org/stable/modules/lda_qda.html -
Principal Component Analysis (PCA) on Iris Data...
projection = "3d" , elev =- 150 , azim = 110 ) X_reduced = PCA ( n_components...PCA fig = plt . figure ( 1 , figsize = ( 8 , 6 )) ax = fig . add_subplot...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html -
set_config — scikit-learn 1.7.0 documentation
assume_finite = None , working_memory = None , print_changed_only = None...None , display = None , pairwise_dist_chunk_size = None , enabl...scikit-learn.org/stable/modules/generated/sklearn.set_config.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 -
GenericUnivariateSelect — scikit-learn 1.7.0 do...
score_func=<function f_classif> , * , mode='percentile' , param=1e-05...chi2 >>> X , y = load_breast_cancer ( return_X_y = True ) >>> X...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.html -
GroupShuffleSplit — scikit-learn 1.7.0 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 -
Ridge coefficients as a function of the L2 Regu...
w = make_regression ( n_samples = 100 , n_features = 10 ,...n_informative = 8 , coef = True , random_state = 1 ) # Obtain...scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_coeffs.html -
IsotonicRegression — scikit-learn 1.7.0 documen...
y_min = None , y_max = None , increasing = True , out_of_bounds...X , y = make_regression ( n_samples = 10 , n_features = 1 , random_state...scikit-learn.org/stable/modules/generated/sklearn.isotonic.IsotonicRegression.html -
export_graphviz — scikit-learn 1.7.0 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