- Sort Score
- Num 10 results
- Language All
- Labels All
Results 1 - 10 of 51 for 1 (0.4 seconds)
Filter
- Size
- - 10KB 36
- 10KB - 100KB 15
- File Type
- Text 51
-
model_evaluation.rst.txt
1, 1, 1, 1, 1] >>> y_pred = [0, 1, 0, 1, 0, 1, 0, 1] >>>...0, 0, 1, 1, 1, 1, 1] >>> y_pred = [0, 1, 0, 1, 0, 1, 0, 1] >>>...scikit-learn.org/stable/_sources/modules/model_evaluation.rst.txt -
cross_validation.rst.txt
[1., 1.], [-1., -1.], [2., 2.]]) >>> y = np.array([0, 1, 0,...3] [0 1] [1 3] [0 2] [1 2] [0 3] [0 3] [1 2] [0 2] [1 3] [0 1]...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
plot_discretization_strategies.rst.txt
len(strategies) + 1, i) ax.scatter(X[:, 0], X[:, 1], edgecolors="k")...300), np.linspace(X[:, 1].min(), X[:, 1].max(), 300), ) grid =...scikit-learn.org/stable/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt -
preprocessing.rst.txt
1. , 0.1], [4.4, 2.2, 1.1, 0.1], [4.4, 2.2, 1.2, 0.1], ...,...0. , -1.22, 1.33 ], [ 1.22, 0. , -0.267], [-1.22, 1.22, -1.06 ]])...scikit-learn.org/stable/_sources/modules/preprocessing.rst.txt -
feature_selection.rst.txt
1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]]...0], [0, 0], [1, 1], [1, 0], [1, 1]]) As expected, ``VarianceThreshold``...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
plot_release_highlights_1_8_0.rst.txt
#sk-container-id-1 pre { padding: 0; } #sk-container-id-1 input.sk-hidden--visually...ound); flex-grow: 1; } #sk-container-id-1 div.sk-parallel { display:...scikit-learn.org/stable/_sources/auto_examples/release_highlights/plot_release_highlights_1_8_0.r... -
clustering.rst.txt
1, 1, 1] >>> labels_pred = [0, 0, 1, 1, 2, 2] >>>...= [0, 0, 0, 1, 1, 1] >>> labels_pred = [0, 0, 1, 1, 2, 2] >>>...scikit-learn.org/stable/_sources/modules/clustering.rst.txt -
ensemble.rst.txt
1, 2, np.nan]).reshape(-1, 1) >>> y = [0, 0, 1, 1] >>> gbdt...np.nan, 1, 2, np.nan]).reshape(-1, 1) >>> y = [0, 1, 0, 0, 1] >>>...scikit-learn.org/stable/_sources/modules/ensemble.rst.txt -
getting_started.rst.txt
dataset is easy array([1., 1., 1., 1., 1.]) Automatic parameter...ransform(X) array([[-1., 1.], [ 1., -1.]]) Sometimes, you want...scikit-learn.org/stable/_sources/getting_started.rst.txt -
neighbors.rst.txt
array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])...np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])...scikit-learn.org/stable/_sources/modules/neighbors.rst.txt