Run
10559708

Run 10559708

Task 3 (Supervised Classification) kr-vs-kp Uploaded 21-10-2020 by Mohit Aggarwal
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Flow

sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear _model._logistic.LogisticRegression)(1)Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and "inverse_transform" if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.
sklearn.linear_model._logistic.LogisticRegression(4)_C1.0
sklearn.linear_model._logistic.LogisticRegression(4)_class_weightnull
sklearn.linear_model._logistic.LogisticRegression(4)_dualfalse
sklearn.linear_model._logistic.LogisticRegression(4)_fit_intercepttrue
sklearn.linear_model._logistic.LogisticRegression(4)_intercept_scaling1
sklearn.linear_model._logistic.LogisticRegression(4)_l1_rationull
sklearn.linear_model._logistic.LogisticRegression(4)_max_iter100
sklearn.linear_model._logistic.LogisticRegression(4)_multi_class"auto"
sklearn.linear_model._logistic.LogisticRegression(4)_n_jobsnull
sklearn.linear_model._logistic.LogisticRegression(4)_penalty"l2"
sklearn.linear_model._logistic.LogisticRegression(4)_random_state41925
sklearn.linear_model._logistic.LogisticRegression(4)_solver"lbfgs"
sklearn.linear_model._logistic.LogisticRegression(4)_tol0.0001
sklearn.linear_model._logistic.LogisticRegression(4)_verbose0
sklearn.linear_model._logistic.LogisticRegression(4)_warm_startfalse
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_cvnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_error_scoreNaN
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_iid"deprecated"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_n_iter10
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_n_jobs-1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_param_distributions{"C": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "scipy.stats._continuous_distns.uniform_gen", "a": 0.0, "b": 1.0, "args": [1, 100], "kwds": {}}}, "penalty": ["l1", "l2", "elasticnet"]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_random_state0
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_refittrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_return_train_scorefalse
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_scoringnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_verbose0

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.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

18 Evaluation measures

Area under ROC curve
0.9953 ± 0.0028
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9712 ± 0.0113
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9423 ± 0.0227
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9107 ± 0.0204
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0482 ± 0.01
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.499 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9712 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
Number of instances
3196
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9713 ± 0.0112
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9712 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9986 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0965 ± 0.02
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4995 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1548 ± 0.0257
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.31 ± 0.0514
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9713 ± 0.0113
Cross-validation details (10-fold Crossvalidation)