Machine Learning: Training, Testing, Evaluation

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Machine Learning: Training, Testing, Evaluation:

Evaluation metrics are tied to machine learning tasks. There are different metrics for the tasks of classification, regression, ranking, clustering, topic modelling, etc. Some metrics, such as precision-recall, are useful for multiple tasks. Classification, regression, and ranking are examples of supervised learning, which constitutes a majority of machine learning applications

Classification Metrics

Classification is about predicting class labels given input data. In binary classification, there are two possible output classes. In the multiclass classification, there are more than two possible classes. I’ll focus on binary classification here. But all of the metrics can be extended to the multiclass scenario. An example of binary classification is spam detection, where the input data could include the email text and metadata (sender, sending time), and the output label is either “spam” or “not spam.”
  

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