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AgglomerativeClustering — scikit-learn 1.5.2 do...
n_clusters = 2 , * , metric = 'euclidean' , memory = None , connectivity...connectivity = None , compute_full_tree = 'auto' , linkage = 'ward'...scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html -
load_svmlight_file — scikit-learn 1.5.2 documen...
zero_based='auto' , query_id=False , offset=0 , length=-1 ) [source]...n_features=None , dtype=<class 'numpy.float64'> , multilabel=False...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_svmlight_file.html -
Ledoit-Wolf vs OAS estimation — scikit-learn 1....
shrinkage_ oa = OAS ( store_precision = False , assume_centered = True...yerr = lw_mse . std ( 1 ), label = "Ledoit-Wolf" , color = "navy"...scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html -
Nested versus non-nested cross-validation — sci...
random_state = i ) outer_cv = KFold ( n_splits = 4 , shuffle = True ,...NUM_TRIALS = 30 # Load the dataset iris = load_iris () X_iris = iris...scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html -
Release Highlights for scikit-learn 0.22 — scik...
y_test = train_test_split ( X , y , random_state = 42 ) svc = SVC...LinearSVC X , y = load_iris ( return_X_y = True ) estimators = [ ( "rf"...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_22_0.html -
Time-related feature engineering — scikit-learn...
figsize = ( 12 , 4 )) y . hist ( bins = 30 , ax = ax ) _ = ax ....axes = plt . subplots ( nrows = 2 , ncols = 3 , figsize = ( 13...scikit-learn.org/stable/auto_examples/applications/plot_cyclical_feature_engineering.html -
Concentration Prior Type Analysis of Variation ...
s = 5 , marker = "o" , color = colors [ y ], alpha = 0.8 )...width = 0.9 , color = "#56B4E9" , zorder = 3 , align = "center"...scikit-learn.org/stable/auto_examples/mixture/plot_concentration_prior.html -
Gaussian processes on discrete data structures ...
width = 0.2 , color = "r" , alpha = 1 , label = "training"...)], s = 100 , marker = "x" , facecolor = "b" , linewidth = 2 ,...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_on_structured_data.html -
preprocessing.rst.txt
_preprocessing: ========== Preprocessing data ========== .. currentmodule::...removal and variance scaling ========== **Standardization** of datasets...scikit-learn.org/stable/_sources/modules/preprocessing.rst.txt -
Demonstrating the different strategies of KBins...
cluster_std = 0.5 , centers = centers_0 , random_state = random_state...cluster_std = 0.5 , centers = centers_1 , random_state = random_state...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_strategies.html