How to generate Classification Report and Confusion Matrix in Python
A classification report includes metrics such as precision, recall, f1-score, and support for each class in the dataset. A confusion matrix, on the other hand, provides a summary of the model’s performance by comparing the predicted and actual class labels.
After that, use the predict() method to make predictions on the test set, and compare the predicted and actual class labels.
To create a classification report, use the classification_report() function, which takes the actual class labels and the predicted class labels as inputs. The function returns a report with the precision, recall, f1-score, and support for each class.
To create a confusion matrix, use the confusion_matrix() function, which takes the actual class labels and the predicted class labels as inputs. The function returns a matrix with the number of true positives, true negatives, false positives, and false negatives for each class.
In summary, a classification report and a confusion matrix are powerful tools for evaluating the performance of a machine learning model. By using the classification_report() and confusion_matrix() functions in scikit-learn, it’s easy to generate these reports in Python, making it a valuable tool for data scientists and machine learning practitioners.
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