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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 -
DetCurveDisplay — scikit-learn 1.7.1 documentation
test_size = 0.4 , random_state = 0 ) >>> clf = SVC ( random_state...DetCurveDisplay ( ... fpr = fpr , fnr = fnr , estimator_name = "SVC" ... )...scikit-learn.org/stable/modules/generated/sklearn.metrics.DetCurveDisplay.html -
check_estimator — scikit-learn 1.7.1 documentation
estimator = None , generate_only = False , * , legacy : bool = True...| None = None , on_skip : Literal [ 'warn' ] | None = 'warn'...scikit-learn.org/stable/modules/generated/sklearn.utils.estimator_checks.check_estimator.html -
LastaFlute の Thymeleaf | LastaFlute
name= "productName" value= "" class= "validError" type= "text"...name= "productName" value= "" class= "validError" type= "text"...dbflute.seasar.org/ja/lastaflute/howto/action/lathymeleaf.html -
Poisson regression and non-normal loss — scikit...
axes = plt . subplots ( nrows = 2 , ncols = 4 , figsize = ( 16..., ax = plt . subplots ( nrows = 2 , ncols = 2 , figsize = ( 12...scikit-learn.org/stable/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html -
Probability calibration of classifiers — scikit...
): this_X = X_train [ y_train == this_y ] this_sw = sw_train [...n_samples = n_samples , centers = centers , shuffle = False , random_state...scikit-learn.org/stable/auto_examples/calibration/plot_calibration.html -
Data tiers | Elastic Docs
v=true&s=node . Edit your cluster from...prefix>/_settings?filter_path=**.index.store.snapshot.snapsh...www.elastic.co/docs/manage-data/lifecycle/data-tiers -
Comparing random forests and the multi-output m...
edgecolor = "k" , c = "navy" , s = s , marker = "s" , alpha = a , label...edgecolor = "k" , c = "c" , s = s , marker = "^" , alpha = a , label...scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html -
Demonstration of k-means assumptions — scikit-l...
( X [ y == 0 ][: 500 ], X [ y == 1 ][: 100 ], X [ y == 2 ][: 10...axs = plt . subplots ( nrows = 2 , ncols = 2 , figsize = ( 12...scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html -
Release Highlights for scikit-learn 1.6 — sciki...
random_state = 0 ) start = time . time () classifier = SGDClassifier...))}, cv = 5 , ) . fit ( X , y , X_val = X_val , y_val = y_val )...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_6_0.html