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9.1. Strategies to scale computationally: bigge...
1.1.1. Streaming instances # Basically, 1. may be a...to make your system scale. 9.1.1. Scaling with instances using...scikit-learn.org/stable/computing/scaling_strategies.html -
1.11. Ensembles: Gradient boosting, random fore...
Gradient Boosting models 1.11.1.1.1. Usage # Most of the parameters...= [[ 1 , 0 ], ... [ 1 , 0 ], ... [ 1 , 0 ], ... [ 0 , 1 ]] >>>...scikit-learn.org/stable/modules/ensemble.html -
CountVectorizer — scikit-learn 1.8.0 docu...
[[0 1 1 1 0 0 1 0 1] [0 2 0 1 0 1 1 0 1] [1 0 0 1 1 0 1 1 1] [0...[[0 0 1 1 0 0 1 0 0 0 0 1 0] [0 1 0 1 0 1 0 1 0 0 1 0 0] [1 0 0...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html -
resample — scikit-learn 1.8.0 documentation
1 , 1 , 1 , 1 , 1 , 1 , 1 ] >>> resample...... random_state = 0 ) [1, 1, 1, 0, 1] On this page This Page...scikit-learn.org/stable/modules/generated/sklearn.utils.resample.html -
NearestNeighbors — scikit-learn 1.8.0 doc...
() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]]) radius_neighbors...() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) set_params...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html -
LabelBinarizer — scikit-learn 1.8.0 docum...
array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 1, 0]]) fit ( y )...fit ( np . array ([[ 0 , 1 , 1 ], [ 1 , 0 , 0 ]])) LabelBinarizer()...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html -
brier_score_loss — scikit-learn 1.8.0 doc...
y_true in {-1, 1} or {0, 1}, pos_label defaults to 1; else if y_true...defined as: \[\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}(y_{ic} - \hat{p}_{ic})^{2}\]...scikit-learn.org/stable/modules/generated/sklearn.metrics.brier_score_loss.html -
precision_score — scikit-learn 1.8.0 docu...
[ 1 , 1 , 1 ], [ 0 , 1 , 1 ]] >>> y_pred...= [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ], [ 1 , 1 , 0 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html -
sparse_encode — scikit-learn 1.8.0 docume...
1 , 0 ], ... [ - 1 , - 1 , 2 ], ... [ 1 , 1 , 1 ], ......>>> X = np . array ([[ - 1 , - 1 , - 1 ], [ 0 , 0 , 3 ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.decomposition.sparse_encode.html -
completeness_score — scikit-learn 1.8.0 d...
1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Non-perfect labelings...completeness_score ([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 0 , 1 ])) 0.0 >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html