Confusion Matrix
Demystifying the Confusion Matrix: A Deep Dive into Evaluating Model Performance In the world of Machine Learning, understanding model performance is essential. One powerful tool for this purpose is the Confusion Matrix, a simple yet highly effective table layout for visualization and comprehension of your classifier’s performance. The confusion matrix places the model’s predictions against the ground-truth data labels, creating an intuitive comparison grid. Each cell in this matrix represents a different aspect of the model’s performance, namely True Negatives (TN), False Negatives (FN), False Positives (FP) and True Positives (TP)....