Run
10436678

Run 10436678

Task 146825 (Supervised Classification) Fashion-MNIST Uploaded 28-01-2020 by Nicolas Hug
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  • openml-python Sklearn_0.23.dev0.
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Flow

sklearn.linear_model._logistic.LogisticRegression(2)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(2)_C1.0
sklearn.linear_model._logistic.LogisticRegression(2)_class_weightnull
sklearn.linear_model._logistic.LogisticRegression(2)_dualfalse
sklearn.linear_model._logistic.LogisticRegression(2)_fit_intercepttrue
sklearn.linear_model._logistic.LogisticRegression(2)_intercept_scaling1
sklearn.linear_model._logistic.LogisticRegression(2)_l1_rationull
sklearn.linear_model._logistic.LogisticRegression(2)_max_iter100
sklearn.linear_model._logistic.LogisticRegression(2)_multi_class"auto"
` for more details">sklearn.linear_model._logistic.LogisticRegression(2)_n_jobsnull
sklearn.linear_model._logistic.LogisticRegression(2)_penalty"l2"
sklearn.linear_model._logistic.LogisticRegression(2)_random_state29697
sklearn.linear_model._logistic.LogisticRegression(2)_solver"lbfgs"
sklearn.linear_model._logistic.LogisticRegression(2)_tol0.0001
sklearn.linear_model._logistic.LogisticRegression(2)_verbose0
sklearn.linear_model._logistic.LogisticRegression(2)_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

0.9844 ± 0.0008
Per class
Cross-validation details (10-fold Crossvalidation)
0.8507 ± 0.0034
Per class
Cross-validation details (10-fold Crossvalidation)
0.8353 ± 0.0039
Cross-validation details (10-fold Crossvalidation)
0.8371 ± 0.0034
Cross-validation details (10-fold Crossvalidation)
0.0438 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
0.18
Cross-validation details (10-fold Crossvalidation)
0.8518 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
70000
Per class
Cross-validation details (10-fold Crossvalidation)
0.8506 ± 0.0036
Per class
Cross-validation details (10-fold Crossvalidation)
0.8518 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
3.3219
Cross-validation details (10-fold Crossvalidation)
0.2434 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
0.3
Cross-validation details (10-fold Crossvalidation)
0.147 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
0.49 ± 0.0055
Cross-validation details (10-fold Crossvalidation)
0.8518 ± 0.0035
Cross-validation details (10-fold Crossvalidation)