Metrics for Evaluating AI/ML Algorithms
Metric for Evaluation (Performance Measures) for AI/ML algorithm A metric is any number that provides measured information. Performance of learning models is evaluated with various types of metrics. Evaluation of machine learning models can be considered similar to hypothesis testing in statistics. In statistics value of the population parameter has to be statistically inferred based on the sample statistics. Similarly an AI/ML model is evaluated using sampled finite data set. The available data set is split into train and test sets. Trained models are never evaluated on train data but on test set. Evaluations can be done by holding out the test set, cross validation or boot strapping. Classification Accuracy Accuracy is the simplest metric to measure the performance of a trained ML. It is the number of correct predictions made divided by the total number of predictions made for a given set of observed data. Figure-1: