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1.5. Stochastic Gradient Descent — scikit-learn...
\(L_2\) norm: \(R(w) := \frac{1}{2} \sum_{j=1}^{m} w_j^2 = ||w||_2^2\)...values: >>> clf . predict ([[ 2. , 2. ]]) array([1]) SGD fits a...scikit-learn.org/stable/modules/sgd.html -
PoissonRegressor — scikit-learn 1.7.1 documenta...
determination R^2. R^2 uses squared error and D^2 uses the deviance...PoissonRegressor () >>> X = [[ 1 , 2 ], [ 2 , 3 ], [ 3 , 4 ], [ 4 , 3...scikit-learn.org/stable/modules/generated/sklearn.linear_model.PoissonRegressor.html -
make_classification — scikit-learn 1.7.1 docume...
n_informative = 2 , n_redundant = 2 , n_repeated = 0 , n_classes = 2 , n_clusters_per_class...n_clusters_per_class = 2 , weights = None , flip_y = 0.01 , class_sep...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html -
enet_path — scikit-learn 1.7.1 documentation
it is: 1 / ( 2 * n_samples ) * || y - Xw ||^ 2_2 + alpha * l1_ratio...|| w ||^ 2_2 For multi-output tasks it is: ( 1 / ( 2 * n_samples...scikit-learn.org/stable/modules/generated/sklearn.linear_model.enet_path.html -
kneighbors_graph — scikit-learn 1.7.1 documenta...
p = 2 , metric_params = None , include_self...standard Euclidean distance when p = 2. See the documentation of scipy.spatial.distance...scikit-learn.org/stable/modules/generated/sklearn.neighbors.kneighbors_graph.html -
sphinx-design.min.css
sd-g-2,.sd-gy-2{--sd-gutter-y: 0.5rem}.sd-g-2,.sd-gx-2{--sd-gutter-x:...!important}.sd-p-2{padding:.5rem !important}.sd-pt-2,.sd-py-2{padding-top:.5rem...scikit-learn.org/stable/_static/sphinx-design.min.css -
PLSRegression — scikit-learn 1.7.1 documentation
[ 2. , 2. , 2. ], [ 2. , 5. , 4. ]] >>> y =...= [[ 0.1 , - 0.2 ], [ 0.9 , 1.1 ], [ 6.2 , 5.9 ], [ 11.9 , 12.3...scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html -
"Christmas has always smelled like oranges to m...
2025 2:34 PM Subscribe 50 years and 29...days. D'oh. posted by clavdivs at 2:37 PM on July 10 [ 7 favorites...www.metafilter.com/209524/Christmas-has-always-smelled-like-oranges-to-me -
confusion_matrix — scikit-learn 1.7.1 documenta...
= [ 2 , 0 , 2 , 2 , 0 , 1 ] >>> y_pred = [ 0 , 0 , 2 , 2 , 0...0 , 2 ] >>> confusion_matrix ( y_true , y_pred ) array([[2, 0,...scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html -
mean_absolute_error — scikit-learn 1.7.1 docume...
2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>> mean_absolute_error...]] >>> y_pred = [[ 0 , 2 ], [ - 1 , 2 ], [ 8 , - 5 ]] >>> mean_absolute_error...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html