A Practical Approach for Predictive Models
Focusing on the often-overlooked stages of developing predictive models, this book emphasizes techniques for optimizing predictor representations and selecting the best subset of predictors. It aims to enhance model performance by addressing critical aspects beyond just the algorithms, providing a comprehensive guide for practitioners in the field.
A comprehensive guide to the principles and practices of building robust predictive models. Tailored for practitioners and data scientists, the book blends theoretical foundations with practical applications across various domains, including healthcare, finance, and marketing. Covering essential topics such as data preprocessing, feature engineering, model evaluation, and advanced machine learning techniques, the book emphasizes real-world problem-solving with clear examples and R code implementations. This resource is indispensable for anyone aiming to master predictive analytics and develop models that drive actionable insights.