Apoorv Tyagi • 6/18/2021

Metrics to evaluate your Machine Learning algorithm

This article provides a comprehensive introduction to key evaluation metrics for machine learning and deep learning models. It explains the importance of measuring model performance on out-of-sample data and covers fundamental concepts such as the confusion matrix, accuracy, precision, recall, F1 score, mean absolute error, R-squared score, root mean squared error, and ROC curves. Aimed at beginners in data science and machine learning, it offers clear definitions and examples to help readers understand how to assess the quality and robustness of their predictive models.

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#Machine Learning #Confusion Matrix #Evaluation Metrics