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1.7. Gaussian Processes — scikit-learn 1.8.0 do...
log-transformed [[ -inf 2.30258509] [ -inf 2.30258509] [ -inf 2.30258509]]...\left(1 + \frac{d(x_i, x_j)^2}{2\alpha l^2}\right)^{-\alpha}\] The...scikit-learn.org/stable/modules/gaussian_process.html -
Novelty detection with Local Outlier Factor (LO...
2 ) X_train = np . r_ [ X + 2 , X - 2 ] # Generate...randn ( 20 , 2 ) X_test = np . r_ [ X + 2 , X - 2 ] # Generate...scikit-learn.org/stable/auto_examples/neighbors/plot_lof_novelty_detection.html -
d2_log_loss_score — scikit-learn 1.8.0 document...
Like R^2, D^2 score may be negative (it need...labels = None ) [source] # \(D^2\) score function, fraction of...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_log_loss_score.html -
Gradient Boosting regression — scikit-learn 1.8...
2 , 2 ) # `labels` argument in boxplot...permutation methods identify the same 2 strongly predictive features but...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html -
mean_poisson_deviance — scikit-learn 1.8.0 docu...
y_true = [ 2 , 0 , 1 , 4 ] >>> y_pred = [ 0.5 , 0.5 , 2. , 2. ] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_poisson_deviance.html -
smacof — scikit-learn 1.8.0 documentation
2 ], [ 1 , 0 , 3 ], [ 2 , 3 , 0 ]]) >>> dissimilarities...metric = True , n_components = 2 , init = None , n_init = 'warn'...scikit-learn.org/stable/modules/generated/sklearn.manifold.smacof.html -
8.3. Generated datasets — scikit-learn 1.8.0 do...
"n_clusters_per_class" : 2 , "n_classes" : 2 }, { "n_informative" : 2 , "n_clusters_per_class"...1 , "n_classes" : 2 }, { "n_informative" : 2 , "n_clusters_per_class"...scikit-learn.org/stable/datasets/sample_generators.html -
LinearRegression — scikit-learn 1.8.0 documenta...
2 ], [ 2 , 2 ], [ 2 , 3 ]]) >>> # y = 1 * x_0 + 2 * x_1...float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html -
Plot the decision surfaces of ensembles of tree...
2 , w_pad = 0.2 , pad = 2.5 ) plt . show ()...pair in ([ 0 , 1 ], [ 0 , 2 ], [ 2 , 3 ]): for model in models...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_iris.html -
dbscan — scikit-learn 1.8.0 documentation
2 ], [ 2 , 2 ], [ 2 , 3 ], [ 8 , 7 ], [ 8...min_samples = 2 ) >>> core_samples array([0, 1, 2, 3, 4]) >>> labels...scikit-learn.org/stable/modules/generated/dbscan-function.html