Greetings, data enthusiasts! Today, we're delving into the fundamental tools that help us understand how well our machine learning models are performing. In this comprehensive guide, we'll break down the confusion matrix, accuracy, precision, recall, and F1 score step by step. Grab your Python toolkit, and let's embark on this journey of model evaluation!
1. Confusion Matrix:
Unveiling the Model’s Blueprint
The confusion matrix is like the blueprint that dissects your model's predictions. It consists of four key elements:
- True Positives (TP): True positives represent instances where the model correctly predicted the positive class. In a medical context, this would be cases where a diagnostic test correctly identifies individuals with a specific condition. Example: In a cancer diagnosis model, a true positive would be when the model correctly identifies a patient with cancer based on the given features.
- True Negatives (TN): True negatives are instances where the model correctly predicted the negative class. In a credit approval model, this…
