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ParameterSampler — scikit-learn 1.6.1 documenta...
>>> rng = np . random . RandomState ( 0 ) >>> param_grid = { 'a'...param_list = list ( ParameterSampler ( param_grid , n_iter = 4 , ......scikit-learn.org/stable/modules/generated/sklearn.model_selection.ParameterSampler.html -
pygments.css
html[data-theme="light"] .highlight pre { line-height: 125%;...125%; } html[data-theme="light"] .highlight td.linenos .normal { color:...scikit-learn.org/stable/_static/pygments.css -
power_transform — scikit-learn 1.6.1 documentation
method = 'yeo-johnson' , * , standardize = True , copy = True )...{‘yeo-johnson’, ‘box-cox’}, default=’yeo-johnson’ The power transform...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.power_transform.html -
eclipse-location-copyrename-snapshot.png
17639795 width=1564, height=720, bitDepth=8, colorType=RGBAlpha, ...translatedKeyword=, text=<x:xmpmeta xmlns:x="adobe:ns:meta/" x:xmptk="XMP...dbflute.seasar.org/data/snapshot/friends/eclipse/install/eclipse-location-copyrename-snapshot.png -
Outlier detection on a real data set — scikit-l...
contamination = 0.25 ), "OCSVM" : OneClassSVM ( nu = 0.25 , gamma = 0.35..., levels = [ 0 ], colors = color , ax = ax , ) legend_lines ....scikit-learn.org/stable/auto_examples/applications/plot_outlier_detection_wine.html -
Explicit feature map approximation for RBF kern...
kernel_svm = svm . SVC ( gamma = 0.2 ) linear_svm = svm . LinearSVC...feature_map_fourier = RBFSampler ( gamma = 0.2 , random_state = 1 ) feature_map_nystroem...scikit-learn.org/stable/auto_examples/miscellaneous/plot_kernel_approximation.html -
Effect of transforming the targets in regressio...
y = make_regression ( n_samples = 10_000 , noise = 100 ,...ax1 ) = plt . subplots ( 1 , 2 , sharey = True ) ridge_cv = RidgeCV...scikit-learn.org/stable/auto_examples/compose/plot_transformed_target.html -
Rothko Chapel | MetaFilter
<div class="svg-sprite" id="svg-sprite-container" style="display:none"></div>...type="text/javascript"> !function() { var static_server = 'dh...www.metafilter.com/208156/Rothko-Chapel -
Novelty detection with Local Outlier Factor (LO...
X_outliers = np . random . uniform ( low =- 4 , high = 4 , size = ( 20...1 ], c = "blueviolet" , s = s , edgecolors = "k" ) c = plt . scatter...scikit-learn.org/stable/auto_examples/neighbors/plot_lof_novelty_detection.html -
RocCurveDisplay — scikit-learn 1.6.1 documentation
pos_label = None , name = None , ax = None , plot_chance_level = False...roc_auc = None , estimator_name = None , pos_label = None ) [source]...scikit-learn.org/stable/modules/generated/sklearn.metrics.RocCurveDisplay.html
