v1.0.9

New metrics (documentation page is in progress)

For binary classification:

• accuracy
• precision
• recall
• f1_score
• specificity
• tp, tn, fp, fn

For multiclass classification:

• micro_averaging_accuracy
• micro_averaging_precision
• micro_averaging_recall
• macro_averaging_accuracy
• macro_averaging_precision
• macro_averaging_recall

For regression:

• mean_squared_error
• root_mean_squared_error
• mean_absolute_error

Examples:

labels = [1,1,1,1,1,0,0,0,0,0]
predictions = [1,1,1,1,1,0,0,0,0,0]
metrics = Tools.Metric.compute_metrics_binary_classification(labels, predictions)

learner = Learning.Scikitlearn("tests/dermatology.csv", learner_type=Learning.CLASSIFICATION)
models = learner.evaluate(method=Learning.K_FOLDS, output=Learning.DT, test_size=0.2)
for id, models in enumerate(models):
     metrics = learner.get_details()[id]["metrics"]