Applied Data Science Coding with Python: How to get Classification Report

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How to get Classification Report

A classification report is a tool used in machine learning to evaluate the performance of a classification model. It is used to compare the predicted classes and the true classes of a dataset. The classification report in Python typically includes several metrics such as precision, recall, f1-score, and support. Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. Recall (Sensitivity) is the ratio of correctly predicted positive observations to the all observations in actual class. F1-Score is the Harmonic Mean between precision and recall. Support is the number of observations of the corresponding class. By using these metrics, a classification report can help you understand how well your model is performing and identify areas for improvement. You can use scikit-learn library’s classification_report() function to generate a classification report in Python.

 

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get Classification Report.

How to get Classification Report

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