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feature_selection.rst.txt
the parameter C controls the sparsity: the smaller C the fewer features...(150, 4) >>> lsvc = LinearSVC(C=0.01, penalty="l1", dual=False).fit(X,...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
6.6. Random Projection — scikit-learn 1.4.2 doc...
scikit-learn.org/stable/modules/random_projection.html -
sklearn.model_selection.ValidationCurveDisplay ...
param_range = "C" , np . logspace ( - 8 , 3 , 10...>>> param_name , param_range = "C" , np . logspace ( - 8 , 3 , 10...scikit-learn.org/stable/modules/generated/sklearn.model_selection.ValidationCurveDisplay.html -
model_evaluation.rst.txt
math:: \frac{1}{c(c-1)}\sum_{j=1}^{c}\sum_{k > j}^c (\text{AUC}(j...math:: \frac{1}{c(c-1)}\sum_{j=1}^{c}\sum_{k > j}^c p(j \cup k)(...scikit-learn.org/stable/_sources/modules/model_evaluation.rst.txt -
decomposition.rst.txt
C. Sorensen, and C. Yang, (1998) |details-end|....org/Boutsidis_PRE_08.pdf>`_ C. Boutsidis, E. Gallopoulos, 2008...scikit-learn.org/stable/_sources/modules/decomposition.rst.txt -
1.1. Linear Models — scikit-learn 1.4.2 documen...
C is given by alpha = 1 / C or alpha = 1 / (n_samples...sklearn.svm.l1_min_c allows to calculate the lower bound for C in order...scikit-learn.org/stable/modules/linear_model.html -
preprocessing.rst.txt
"b" and "c" are their own categories, unknown...... [["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3 + [np.nan]],...scikit-learn.org/stable/_sources/modules/preprocessing.rst.txt -
sklearn.metrics.hinge_loss — scikit-learn 1.4.2...
scikit-learn.org/stable/modules/generated/sklearn.metrics.hinge_loss.html -
plot_pca_iris.py
c=y, cmap=plt.cm.nipy_spectral,...scikit-learn.org/stable/_downloads/1168f82083b3e70f31672e7c33738f8d/plot_pca_iris.py -
An introduction to machine learning with scikit...
C = 100. ) The clf (for classifier)...digits . target [: - 1 ]) SVC(C=100.0, gamma=0.001) Now you can...scikit-learn.org/stable/tutorial/basic/tutorial.html