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Task 119 (Supervised Classification) diabetes Uploaded 22-01-2024 by Continuous Integration
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TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)	Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the cross-entropy loss if the 'multi_class' option is set to 'multinomial'. (Currently the 'multinomial' option is supported only by the 'lbfgs', 'sag', 'saga' and 'newton-cg' solvers.) This class implements regularized logistic regression using the 'liblinear' library, 'newton-cg', 'sag', 'saga' and 'lbfgs' solvers. Note that regularization is applied by default. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance; any other input format will be converted (and copied). The 'newton-cg', 'sag', and 'lbfgs' solvers support only L2 regularization with primal formulation, or no regularization. The 'liblinear' solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. The Elastic-Net regularization is only su...
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_C	1.0
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_class_weight	null
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_dual	false
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_fit_intercept	true
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_intercept_scaling	1
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_l1_ratio	null
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_max_iter	1000
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_multi_class	"auto"
` for more details">TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_n_jobs	null
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_penalty	"l2"
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_random_state	62501
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_solver	"lbfgs"
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_tol	0.0001
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_verbose	0
TEST767dfa6889sklearn.linear_model._logistic.LogisticRegression(1)_warm_start	false