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linear_model.rst.txt
x_1 + w_2 x_2 + w_3 x_1 x_2 + w_4 x_1^2 + w_5 x_2^2 The (sometimes...:math:`[x_1, x_2]` to :math:`[1, x_1, x_2, x_1^2, x_1 x_2, x_2^2]`, and...scikit-learn.org/stable/_sources/modules/linear_model.rst.txt -
cross_validation.rst.txt
2, 2, 2, 2] >>> groups = [1, 1, 2, 2, 3, 3, 3] >>>...test)) [2 3] [0 1] [1 3] [0 2] [1 2] [0 3] [0 3] [1 2] [0 2] [1 3]...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
model_evaluation.rst.txt
y_true = [2, 0, 2, 2, 0, 1] >>> y_pred = [0, 0, 2, 2, 0, 2] >>> ...y_true = [2, 0, 2, 2, 0, 1] >>> y_pred = [0, 0, 2, 2, 0, 2] >>> ...scikit-learn.org/stable/_sources/modules/model_evaluation.rst.txt -
feature_selection.rst.txt
k=2).fit_transform(X, y) >>> X_new.shape (150, 2) These...estimation. Note that the :math:`\chi^2`-test should only be applied to...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
about.rst.txt
grid:: 2 2 4 4 :class-row: image-subgrid...scikit-learn.org/stable/_sources/about.rst.txt -
feature_extraction.rst.txt
2.0986]}{\sqrt{\big(3^2 + 0^2 + 2.0986^2\big)}} = [...\frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 + v{_2}^2 + \dots + v{_n}^2}}`....scikit-learn.org/stable/_sources/modules/feature_extraction.rst.txt -
contributing.rst.txt
2. Fork the `project repository...scikit-learn 4. Follow steps 2-6 in :ref:`install_bleeding_edge`...scikit-learn.org/dev/_sources/developers/contributing.rst.txt -
supervised_learning.rst.txt
toctree:: :maxdepth: 2 modules/linear_model modules/lda_qda.rst...scikit-learn.org/stable/_sources/supervised_learning.rst.txt -
neighbors.rst.txt
[-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> nbrs...np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> kdt...scikit-learn.org/stable/_sources/modules/neighbors.rst.txt -
ensemble.rst.txt
make_hastie_10_2 >>> X, y = make_hastie_10_2(random_state=0)...x_1' \implies F(x_1, x_2) \leq F(x_1', x_2) - a **monotonic decrease...scikit-learn.org/stable/_sources/modules/ensemble.rst.txt