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1.8. Cross decomposition — scikit-learn 1.7.1 d...
using the rotation matrix \(V(\Delta^T V)^{-1}\) , accessed via the...to compute \(u_k\) and \(v_k\) , \(v_k\) is never normalized....scikit-learn.org/stable/modules/cross_decomposition.html -
feature_extraction.rst.txt
:math:`v_{norm} = \frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 +...:math:`v_{norm} = \frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 +...scikit-learn.org/stable/_sources/modules/feature_extraction.rst.txt -
homogeneity_score — scikit-learn 1.7.1 document...
v_measure_score V-Measure (NMI with arithmetic...Rosenberg and Julia Hirschberg, 2007. V-Measure: A conditional entropy-based...scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_score.html -
Bunch — scikit-learn 1.7.1 documentation
scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html -
plot_kmeans_digits.rst.txt
score compl completeness score v-meas V measure ARI adjusted Rand...metrics.completeness_score, metrics.v_measure_score, metrics.adjusted_rand_score,...scikit-learn.org/stable/_sources/auto_examples/cluster/plot_kmeans_digits.rst.txt -
bootstrap.js
v=d.y,y=void 0===v?0:v,w="function"==typeof...d 0===_?u:_,v=i.elementContext,y=void 0===v?f:v,w=i.altBoundary,E=void...scikit-learn.org/stable/_static/scripts/bootstrap.js -
MiniBatchDictionaryLearning — scikit-learn 1.7....
V ^* ) = argmin 0.5 || X - U V || _Fro ^ 2 + alpha...* || U || _1 , 1 ( U , V ) with || V_k || _2 <= 1 for all 0 <=...scikit-learn.org/stable/modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.html -
1.2. Linear and Quadratic Discriminant Analysis...
X_k^tX_k = \frac{1}{n - 1} V S^2 V^t\) where \(V\) comes from the SVD...(centered) matrix: \(X_k = U S V^t\) . It turns out that we can...scikit-learn.org/stable/modules/lda_qda.html -
BernoulliRBM — scikit-learn 1.7.1 documentation
gibbs ( v ) [source] # Perform one Gibbs...Gibbs sampling step. Parameters : v ndarray of shape (n_samples, n_features)...scikit-learn.org/stable/modules/generated/sklearn.neural_network.BernoulliRBM.html -
RegressorMixin — scikit-learn 1.7.1 documentation
is defined as \((1 - \frac{u}{v})\) , where \(u\) is the residual...((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares...scikit-learn.org/stable/modules/generated/sklearn.base.RegressorMixin.html