Building and Validating Statistical and ML-Based Models for Prediction

Learn to construct and validate statistical and machine learning models for predictive analytics in this 34-minute lecture from NPTEL-NOC IITM. Explore the fundamental principles of building robust predictive models, covering both traditional statistical approaches and modern machine learning techniques. Discover essential validation methodologies to assess model performance and reliability, including cross-validation techniques, performance metrics, and strategies to avoid overfitting. Gain practical insights into model selection criteria, feature engineering considerations, and the importance of proper data splitting for training and testing. Understand how to evaluate model accuracy, precision, recall, and other key performance indicators to ensure your predictive models are both statistically sound and practically applicable across various domains.