Machine Learning Challenge: Day 12
Notebook Link: Click Here for Code Evaluating the Performance of a Classification Model Evaluating the Performance of a Classification Model: Metrics and Techniques ¶ Confusion Matrix Accuracy Recall Precision F1 Classification Report RoC Precision-Recall Curve Cumulative Gains Plot Lift Curve Class Balance Class Prediction Error Discrimination Threshold 1) Confusion Matrix ¶ A confusion matrix is a table used to evaluate the performance of a binary or multiclass classification model. It shows the number of correct and incorrect predictions made by the model, grouped by each class. The confusion matrix provides information about false negatives, false positives, true negatives, and true positives, which are used to compute various evaluation metrics. In [1]: #to plots the model performance metrics import matplotlib.pyplot as plt import seaborn as sns from matplotlib i...