Top 6 Evaluation Metrics in Machine Learning

Learn about the most common evaluation metrics used in Machine Learning

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Machine learning is a rapidly growing field that has been successfully applied to various applications such as image recognition, natural language processing, and predictive modeling. However, building a machine learning model is not the end goal, as it is important to evaluate the performance of the model. Evaluation metrics help in measuring the performance of the model by quantifying how well the model performs on a given task.

Let’s look at the most common evaluation metrics used in machine learning.

Accuracy

Accuracy is a popular evaluation metric that measures the percentage of correctly predicted instances in a dataset. It is calculated by dividing the number of correct predictions by the total number of predictions. The formula for accuracy is:

accuracy = (true positives + true negatives) / (true positives + false positives + true negatives + false negatives)

where true positives (TP) are the number of correct positive predictions, true negatives (TN) are the number of correct negative predictions, false positives (FP) are the number of incorrect positive predictions, and false negatives (FN) are the number of…