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SVM Margins Example — scikit-learn 1.8.0 ...
2 ) - [ 2 , 2 ], np . random . randn ( 20 , 2 ) + [ 2 , 2...This is sqrt(1+a^2) away vertically in # 2-d. margin = 1 / np...scikit-learn.org/stable/auto_examples/svm/plot_svm_margin.html -
classification_report — scikit-learn 1.8....
2 , 2 , 2 ] >>> y_pred = [ 0 , 0 , 2 , 2 , 1 ]...sample_weight = None , digits = 2 , output_dict = False , zero_division...scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html -
trustworthiness — scikit-learn 1.8.0 docu...
defined as \[T(k) = 1 - \frac{2}{nk (2n - 3k - 1)} \sum^n_{i=1}...Should be fewer than n_samples / 2 to ensure the trustworthiness...scikit-learn.org/stable/modules/generated/sklearn.manifold.trustworthiness.html -
HuberRegressor vs Ridge on dataset with strong ...
2.0 , size = 4 ) X_outliers [: 2 , :] += X . max...y_outliers [: 2 ] += y . min () - y . mean () / 4.0 y_outliers [ 2 :] +=...scikit-learn.org/stable/auto_examples/linear_model/plot_huber_vs_ridge.html -
ARDRegression — scikit-learn 1.8.0 docume...
[ 2 , 2 ]], [ 0 , 1 , 2 ]) ARDRegression() >>>...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.ARDRegression.html -
auc — scikit-learn 1.8.0 documentation
2 , 2 ]) >>> y_score = np...y_true , y_score , pos_label = 2 ) >>> metrics . auc (...scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html -
LocallyLinearEmbedding — scikit-learn 1.8...
(n_components + 1) / 2 . see reference [2] modified : use the...n_neighbors = 5 , n_components = 2 , reg = 0.001 , eigen_solver =...scikit-learn.org/stable/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html -
Learn about regression algorithms - IBM Developer
y = w 0 + w 1 x 1 + w 2 x 2 + .... + w n * x n In the following...regression. y = w 0 + w 1 x 1 + w 2 x 2 1 + .... + w n * x n n Even...developer.ibm.com/learningpaths/learning-path-machine-learning-for-developers/learn-regression-al... -
Plot the decision surface of decision trees tra...
2 ], [ 0 , 3 ], [ 1 , 2 ], [ 1 , 3 ], [ 2 , 3 ]]): #...boundary ax = plt . subplot ( 2 , 3 , pairidx + 1 ) plt . tight_layout...scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html -
7.9. Transforming the prediction target (y) ...
y = [[ 2 , 3 , 4 ], [ 2 ], [ 0 , 1 , 3 ], [ 0 , 1 , 2 , 3 , 4...>>> lb . fit ([ 1 , 2 , 6 , 4 , 2 ]) LabelBinarizer() >>>...scikit-learn.org/stable/modules/preprocessing_targets.html