A model with AUC of 0.50 or less is worthless.ĪucPR or Area under the curve of a Precision-Recall curve: Useful measure of success of prediction when the classes are imbalanced (highly skewed datasets). It should be greater than 0.50 for a model to be acceptable. When the test data is unbalanced (where most of the instances belong to one of the classes), the dataset is small, or scores approach 0.00 or 1.00, then accuracy doesn't really capture the effectiveness of a classifier and you need to check additional metrics.ĪucROC or Area under the curve measures the area under the curve created by sweeping the true positive rate vs. But exactly 1.00 indicates an issue (commonly: label/target leakage, over-fitting, or testing with training data). It works well if there are similar number of samples belonging to each class. It is the ratio of number of correct predictions to the total number of input samples. Evaluation metrics for Binary Classification MetricsĪccuracy is the proportion of correct predictions with a test data set. And for clustering, evaluation is based on how close clustered items are to each other, and how much separation there is between the clusters. Understand the metrics used to evaluate an ML.NET model.Įvaluation metrics are specific to the type of machine learning task that a model performs.įor example, for the classification task, the model is evaluated by measuring how well a predicted category matches the actual category.
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