Evaluation is an essential part of machine learning. The evaluation result tells us how well a particular machine learning algorithm performs. Evaluation also helps to explain why specific models have specific behavior and provide directions to improve performance.

In this series, we will focus on the evaluation of the classification tasks. That is to identify which classes certain instances belong to given historical observations. I will explain the concept of several evaluation metrics and their application in Python. Here is the road map of this series:

• Part I: confusion matrix.
• Part II: accuracy, recall, precision, f1-score.
• Part III: soft-metrics: ROC curve, precision-recall curve, ROC-AUC, etc.
• Part IV: ML performance visualization
• Part I: confusion matrix
• Part II: accuracy, recall, precision, f1-score
• Part III: soft-metrics: ROC curve, precision-recall curve, ROC-AUC, etc.
• Part IV: ML performance visualization

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Intro

If this is the first time come across confusion matrix, as the name suggested, it can be confusing. Because there exist so many related terminologies and so many formulas, if you open Wikipedia, you are very likely to get lost.

In a nutshell, a confusion matrix is an evaluation metric for classification problems. But why we need a confusion matrix?

#confusion-matrix #classification #machine-learning #evaluation #deep learning