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heri-eval(1) heri-eval(1)


heri-eval - evaluate classification algorithm


heri-eval [OPTIONS] dataset [-- SVM_TRAIN_OPTIONS]


heri-eval runs training algorithm on dataset and then evaluate it using testing set, specified by option -e. If option -n was applied, cross-validation is used for evaluation, training and testing on different folds are run in parallel, thus utilizing available CPUs. If -r is used, the dataset is splitted into training and testing datasets randomly with the specified ratio, and then holdout is run.


Display help information.
Enable output of per-fold statistics. See -Mf.
Enable T*N-fold cross-validation mode and set the number of folds to N.
Split the dataset into training and testing parts with the specified ratio of their sizes (in percents).
Enable T*N-fold cross-validation mode and set the number of runs to T which 1 by default.
Enable hold-out mode and set the testing dataset.
Set the minimum threshold for making a classification decision. If this flag is applied, micro-average precision, recall, and F1 are calculated instead of accuracy.
Save predictions from testing sets to the specified file.

Format: outcome_class prediction_class [score]

Save incorrectly classified objects to the specified file.

Format: #object_number: outcome_class prediction_class [score])

Save confusion matrix to the specified file.

Format: frequency : outcome_class prediction_class

Pass the specified opts to heri-stat(1).
Pass the specified opts to heri-split(1).
Sets the output mode where chars are: t -- output total statistics, f -- output per-fold statistics, c -- output cross-fold statistics. The default is "-M tc".
Pass the specified seed to heri-split(1).
Keep temporary directory after exiting.
Turn on the debugging mode, implies -K.


 heri-eval -e testing_set.libsvm training_set.libsvm -- -s 0 -t 0
 export SVM_TRAIN_CMD='liblinear-train'
 export SVM_PREDICT_CMD='liblinear-predict'
 heri-eval -p '-mr' -n 5 training_set.libsvm -- -s 4 -q
 heri-eval -p '-mr' -n 5 training_set.libsvm -- -s 4 -q
 export SVM_TRAIN_CMD='scikit_rf-train --estimators=400'
 export SVM_PREDICT_CMD='scikit_rf-predict'
 heri-eval -p '-c' -Mt -t 50 -r 70 dataset.libsvm


Training utility, e.g., liblinear-train (the default is svm-train).
Predicting utility, e.g., liblinear-predict (the default is svm-predict).
Utility for calculating statistics (the default is heri-stat(1)).
Utility for calculating additional statistics (the default is heri-stat-addons(1)).
Utility for splitting the dataset (the default is heri-split(1)).
Temporary directory (the default is /tmp).




heri-split(1) heri-stat(1)