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2.7. Novelty and Outlier Detection — scikit-lea...
data set. References Rousseeuw, P.J., Van Driessen, K. “A fast...train any tool. 2.7.3.1. Fitting an elliptic envelope # One common...scikit-learn.org/stable/modules/outlier_detection.html -
3.5. Validation curves: plotting scores to eval...
0.9 , 0.96, 0.9 ], [0.9, 0.83, 0.96, 0.96, 0.93], [1. , 0.93,...0.94, 0.91, 0.89, 0.92], [0.9 , 0.92, 0.93, 0.92, 0.93], [0.97, 1...scikit-learn.org/stable/modules/learning_curve.html -
2.9. Neural network models (unsupervised) — sci...
belief nets” , G. Hinton, S. Osindero, Y.-W. Teh, 2006 “Training...= \frac{1}{1 + e^{-x}}\] 2.9.1.3. Stochastic Maximum Likelihood...scikit-learn.org/stable/modules/neural_networks_unsupervised.html -
5. Inspection — scikit-learn 1.7.1 documentation
expectation (ICE) plot 5.1.3. Mathematical Definition 5.1.4. Computation...Expectation plots 5.1.1. Partial dependence plots 5.1.2. Individual conditional...scikit-learn.org/stable/inspection.html -
max_error — scikit-learn 1.7.1 documentation
y_true = [ 3 , 2 , 7 , 1 ] >>> y_pred = [ 4 , 2 , 7 , 1 ] >>> max_error...value (the best value is 0.0). Examples >>> from sklearn.metrics...scikit-learn.org/stable/modules/generated/sklearn.metrics.max_error.html -
MLPClassifier — scikit-learn 1.7.1 documentation
function, returns f(x) = 1 / (1 + exp(-x)). ‘tanh’, the hyperbolic tan...function, returns f(x) = max(0, x) solver {‘lbfgs’, ‘sgd’, ‘adam’},...scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html -
GaussianNB — scikit-learn 1.7.1 documentation
- 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2 , 1...1 ], [ 3 , 2 ]]) >>> Y = np . array ([ 1 , 1 , 1 , 2 , 2 , 2...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html -
DummyRegressor — scikit-learn 1.7.1 documentation
X = np . array ([ 1.0 , 2.0 , 3.0 , 4.0 ]) >>> y = np . array...array ([ 2.0 , 3.0 , 5.0 , 10.0 ]) >>> dummy_regr = DummyRegressor...scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyRegressor.html -
fbeta_score — scikit-learn 1.7.1 documentation
weights. zero_division {“warn”, 0.0, 1.0, np.nan}, default=”warn”...average {‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None,...scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html -
precision_recall_fscore_support — scikit-learn ...
weights. zero_division {“warn”, 0.0, 1.0, np.nan}, default=”warn”...average {‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None,...scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html