Mindforge ML | Unit 4 – Podcast 06_Title: Model Evaluation and Engineering Decisions

Building a model is only half the process — evaluating it correctly is critical.

This episode explains performance metrics, confusion matrix analysis, bias–variance tradeoff and model comparison strategies.

Key topics:

• Confusion Matrix: TP, TN, FP and FN interpretation.

• Performance Metrics: Accuracy, Precision, Recall and F1 Score.

• Overfitting vs Underfitting: Bias–variance understanding.

• Cross Validation: Reliable model assessment.

This episode concludes Unit 4 and prepares the foundation for Unsupervised Learning.

Series: Mindforge ML

Produced by: Chatake Innoworks Pvt. Ltd.

Initiative: MindforgeAI