Run
10437877

Run 10437877

Task 145804 (Supervised Classification) tic-tac-toe Uploaded 28-03-2020 by George Volkov
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Flow

sklearn.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...
sklearn.linear_model._logistic.LogisticRegression(1)_C1.0
sklearn.linear_model._logistic.LogisticRegression(1)_class_weightnull
sklearn.linear_model._logistic.LogisticRegression(1)_dualfalse
sklearn.linear_model._logistic.LogisticRegression(1)_fit_intercepttrue
sklearn.linear_model._logistic.LogisticRegression(1)_intercept_scaling1
sklearn.linear_model._logistic.LogisticRegression(1)_l1_rationull
sklearn.linear_model._logistic.LogisticRegression(1)_max_iter100
sklearn.linear_model._logistic.LogisticRegression(1)_multi_class"auto"
sklearn.linear_model._logistic.LogisticRegression(1)_n_jobsnull
sklearn.linear_model._logistic.LogisticRegression(1)_penalty"l2"
sklearn.linear_model._logistic.LogisticRegression(1)_random_state51702
sklearn.linear_model._logistic.LogisticRegression(1)_solver"lbfgs"
sklearn.linear_model._logistic.LogisticRegression(1)_tol0.0001
sklearn.linear_model._logistic.LogisticRegression(1)_verbose0
sklearn.linear_model._logistic.LogisticRegression(1)_warm_startfalse

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

0.6133 ± 0.0562
Per class
Cross-validation details (10-fold Crossvalidation)
0.6523 ± 0.043
Per class
Cross-validation details (10-fold Crossvalidation)
0.2225 ± 0.0929
Cross-validation details (10-fold Crossvalidation)
0.0492 ± 0.0291
Cross-validation details (10-fold Crossvalidation)
0.4272 ± 0.0096
Cross-validation details (10-fold Crossvalidation)
0.453 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
0.7004 ± 0.033
Cross-validation details (10-fold Crossvalidation)
958
Per class
Cross-validation details (10-fold Crossvalidation)
0.6968 ± 0.0527
Per class
Cross-validation details (10-fold Crossvalidation)
0.7004 ± 0.033
Cross-validation details (10-fold Crossvalidation)
0.931 ± 0.0039
Cross-validation details (10-fold Crossvalidation)
0.943 ± 0.0213
Cross-validation details (10-fold Crossvalidation)
0.4759 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
0.4633 ± 0.0105
Cross-validation details (10-fold Crossvalidation)
0.9735 ± 0.0218
Cross-validation details (10-fold Crossvalidation)
0.5946 ± 0.0408
Cross-validation details (10-fold Crossvalidation)