Statistical Prediction and Machine Learning

Statistical Prediction and Machine Learning

John Tuhao Chen, Clement Lee, Lincy Y. Chen

Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter on unsupervised learning and a chapter on statistical learning with data sequentially or simultaneously from multiple resources.

One of the distinct features of this book is the comprehensive coverage of the topics in statistical learning and medical applications. It summarizes the authors’ teaching, research, and consulting experience in which they use data analytics. The illustrating examples and accompanying materials heavily emphasize understanding on data analysis, producing accurate interpretations, and discovering hidden assumptions associated with various methods.

Key

Unifies conventional model-based framework and contemporary data-driven methods into a single overarching umbrella over data science. Includes real-life medical applications in hypertension, stroke, diabetes, thrombolysis, aspirin efficacy. Integrates statistical theory with machine learning algorithms. Includes potential methodological developments in data science.

Publisher

Chapman and Hall/CRC

Publication Date

10/7/2024

ISBN

9780367332273

Pages

314

Categories

Mathematics

Questions & Answers

The book bridges the gap between conventional statistical methods and contemporary machine learning techniques by unifying both approaches under the umbrella of data science. It starts with foundational concepts in statistics and gradually introduces machine learning algorithms. It emphasizes the importance of understanding both model-based and data-driven approaches, highlighting the strengths and limitations of each. The book covers common supervised learning methods like regressions, classification, and tree algorithms, and also discusses unsupervised learning and statistical learning with multiple data sources. It integrates statistical theory with machine learning algorithms and includes real-life medical applications to illustrate the practical use of these techniques. The book aims to provide a comprehensive understanding of data science by bridging the knowledge gap between traditional statistics and modern machine learning.

The book highlights key differences and similarities between model-based and data-driven approaches in data science. Both approaches aim to predict outcomes from data but differ in their foundational assumptions and methodologies.

Differences:

  • Model-based assumes a specific underlying model for the data, like linear regression or logistic regression, and focuses on parameter estimation and hypothesis testing. It requires plausible model assumptions and can be sensitive to small sample sizes.
  • Data-driven does not assume a specific model and instead learns from the data patterns. It often uses algorithms like decision trees, neural networks, and clustering, which can be more robust to complex data but may overfit if not properly regularized.

Similarities:

  • Both approaches aim to minimize prediction error and improve accuracy.
  • They both require data preprocessing and feature selection.
  • Both can benefit from techniques like cross-validation and bootstrapping to assess model performance.
  • Both are essential in data science and can be used in conjunction to address different aspects of a data analysis problem.

The book addresses the trade-offs between bias and variance in predictive models by discussing the concept of bias-variance trade-off, which is a fundamental issue in data science. It explains that reducing bias often leads to higher variance, and vice versa. The book proposes several solutions to manage this trade-off:

  1. Minimum Variance Unbiased Estimators (UMVUE): It introduces UMVUE as a method to minimize variance while maintaining unbiasedness. This approach is particularly useful when the goal is to minimize the squared prediction error.

  2. Minimum Risk Estimators (MRE): The book extends the concept of UMVUE to MRE, which considers the risk function, not just variance. MRE can be more effective than UMVUE when a small amount of bias can significantly reduce variance.

  3. Regularization Techniques: It discusses regularization methods like Ridge and LASSO regression, which add a penalty term to the loss function to control the complexity of the model, thus balancing bias and variance.

  4. Cross-Validation: The book emphasizes the use of cross-validation to assess model performance and select the best model that balances bias and variance.

By exploring these methods, the book provides a comprehensive understanding of how to manage the bias-variance trade-off in predictive models.

The book covers a range of supervised and unsupervised learning methods. For supervised learning, it includes regressions (like linear regression), classification (like logistic regression), support vector machines, tree algorithms (like decision trees), and range regressions. These are illustrated with examples such as predicting insurance premiums based on driving years and analyzing wine preferences using physicochemical properties.

For unsupervised learning, the book discusses methods like K-means clustering and principal component analysis. K-means clustering is exemplified through consumer preference clustering for marketing strategies, while principal component analysis is illustrated through its application in analyzing the impact of acidity on wine taste. These examples demonstrate the practical application of these methods in various fields.

The book emphasizes the importance of understanding underlying assumptions and interpretations of data analysis methods through several key approaches. It starts by highlighting the two primary cultures in data science: model-based and data-driven. It illustrates how incorrect assumptions can lead to "correct answers to the wrong problem," emphasizing the need for careful consideration of model assumptions. The book also emphasizes the significance of interpreting results accurately and uncovering hidden assumptions associated with various methods. It provides comprehensive coverage of statistical learning topics, integrating statistical theory with machine learning algorithms, and includes real-life medical applications to demonstrate the practical implications of understanding these assumptions and interpretations. Additionally, the book offers examples and materials that focus on data analysis, interpretation, and the identification of hidden assumptions, guiding readers towards effective application of statistical learning methods.

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