Stories with Tag Decision Trees

• Understanding Machine Learning: From Theory to Algorithms

  permalink

  Posted: 2025-04-04 18:25:23

  Verbose tl;dr

  "Understanding Machine Learning: From Theory to Algorithms" provides a comprehensive overview of machine learning, bridging the gap between theoretical principles and practical applications. The book covers a wide range of topics, from basic concepts like supervised and unsupervised learning to advanced techniques like Support Vector Machines, boosting, and dimensionality reduction. It emphasizes the theoretical foundations, including statistical learning theory and PAC learning, to provide a deep understanding of why and when different algorithms work. Practical aspects are also addressed through the presentation of efficient algorithms and their implementation considerations. The book aims to equip readers with the necessary tools to both analyze existing learning algorithms and design new ones.

  "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David offers a comprehensive exploration of the fascinating field of machine learning, bridging the gap between theoretical foundations and practical algorithmic implementations. The book meticulously constructs a conceptual framework for understanding how machines learn from data, starting with fundamental concepts like the Probably Approximately Correct (PAC) learning model. This model provides a rigorous mathematical framework for analyzing the ability of learning algorithms to generalize from a limited set of training examples to unseen data, taking into account factors such as sample complexity, error rates, and computational efficiency.

  The authors delve into the core tenets of learnability, examining the conditions under which a concept can be effectively learned by a machine. They discuss various hypothesis classes and their representational power, highlighting the trade-off between expressiveness and the risk of overfitting, where a model learns the training data too well and fails to generalize to new instances. The book extensively covers key learning paradigms, including supervised learning, unsupervised learning, and reinforcement learning. Within supervised learning, specific techniques such as linear regression, logistic regression, support vector machines, and decision trees are explored in detail, both in terms of their mathematical underpinnings and practical implementation considerations.

  Unsupervised learning, which involves learning patterns from unlabeled data, is also given considerable attention. Clustering algorithms, dimensionality reduction techniques, and generative models are discussed, providing the reader with a diverse toolkit for extracting knowledge from unstructured data. Furthermore, the book touches upon the exciting field of reinforcement learning, where agents learn to interact with an environment to maximize rewards, introducing fundamental concepts like Markov Decision Processes and various reinforcement learning algorithms.

  A significant portion of the book is dedicated to a rigorous treatment of the theoretical foundations of machine learning. Concepts like Rademacher complexity, VC dimension, and stability are introduced and used to derive generalization bounds for different learning algorithms. These theoretical tools provide valuable insights into the behavior of learning algorithms and help explain why certain algorithms perform better than others in specific scenarios. The authors also address the computational aspects of machine learning, discussing optimization algorithms and their role in training complex models efficiently. They explore techniques such as gradient descent, stochastic gradient descent, and convex optimization, providing a thorough understanding of how these methods are used to find optimal model parameters.

  Beyond the core theoretical and algorithmic concepts, the book also touches upon more advanced topics, including online learning, multi-class classification, structured output prediction, and learning theory in the context of non-i.i.d. data. Throughout the text, the authors maintain a balance between theoretical rigor and practical applicability, providing numerous examples, illustrations, and exercises to help the reader solidify their understanding. This detailed and comprehensive approach makes the book a valuable resource for both students embarking on their machine learning journey and seasoned practitioners seeking to deepen their understanding of the field's theoretical foundations.

  • machine learning
  • algorithms
  • theory
  • supervised learning
  • Unsupervised Learning
  • reinforcement learning
  • statistical learning theory
  • PAC learning
  • VC dimension
  • bias-variance tradeoff
  • Overfitting
  • regularization
  • model selection
  • optimization
  • Gradient Descent
  • linear regression
  • logistic regression
  • support vector machines
  • Decision Trees
  • neural networks
  • deep learning
  • clustering
  • dimensionality reduction
  • Computer Science
  • artificial intelligence
  • Data Science

  Summary of Comments ( 45 )
  https://news.ycombinator.com/item?id=43586073

  Verbose tl;dr

  HN users largely praised Shai Shalev-Shwartz and Shai Ben-David's "Understanding Machine Learning" as a highly accessible and comprehensive introduction to the field. Commenters highlighted the book's clear explanations of fundamental concepts, its rigorous yet approachable mathematical treatment, and the helpful inclusion of exercises. Several pointed out its value for both beginners and those with prior ML experience seeking a deeper theoretical understanding. Some compared it favorably to other popular ML resources, noting its superior balance between theory and practice. A few commenters also shared specific chapters or sections they found particularly insightful, such as the treatment of PAC learning and the VC dimension. There was a brief discussion on the book's coverage (or lack thereof) of certain advanced topics like deep learning, but the overall sentiment remained strongly positive.

  The Hacker News post titled "Understanding Machine Learning: From Theory to Algorithms" linking to Shai Shalev-Shwartz and Shai Ben-David's book has a moderate number of comments, discussing various aspects of the book and machine learning education in general.

  Several commenters praise the book for its clarity and accessibility, especially for those with a stronger mathematical background. One user describes it as the "most digestible theory book," highlighting its helpful explanations of fundamental concepts. Another appreciates the book's focus on proving the theory behind ML algorithms, which they found lacking in other resources. The balance between theory and practical application is also commended, with some users noting how the book helped them bridge the gap between abstract concepts and real-world implementations. Specific chapters on PAC learning and VC dimension are singled out as particularly valuable.

  A recurring theme in the comments is the comparison of this book with other popular machine learning resources. "The Elements of Statistical Learning" is frequently mentioned as a more statistically-focused alternative, often considered more challenging. Some users suggest using both books in conjunction, leveraging Shalev-Shwartz and Ben-David's book as a starting point before tackling the more advanced "Elements of Statistical Learning." Another comparison is made with the "Hands-On Machine Learning" book, which is characterized as more practically oriented.

  Some commenters discuss the role of mathematical prerequisites in understanding machine learning. While the book is generally praised for its clarity, a few users acknowledge that a solid foundation in linear algebra, probability, and calculus is still necessary to fully grasp the material. One comment even suggests specific resources to brush up on these mathematical concepts before diving into the book.

  Beyond the book itself, the discussion touches upon broader topics in machine learning education. The importance of understanding the theoretical underpinnings of algorithms is emphasized, with several comments cautioning against relying solely on practical implementations without a deeper understanding of the underlying principles. The evolving nature of the field is also acknowledged, with some users mentioning more recent advancements that aren't covered in the book. Finally, there's a brief discussion about the role of online courses versus traditional textbooks in learning machine learning, with varying opinions on their respective merits.

• Coping with dumb LLMs using classic ML

  permalink

  Posted: 2025-01-22 09:25:07

  Verbose tl;dr

  The blog post explores using traditional machine learning (specifically, decision trees) to interpret and refine the output of less capable or "dumb" Large Language Models (LLMs). The author describes a scenario where an LLM is tasked with classifying customer service tickets, but its performance is unreliable. Instead of relying solely on the LLM's classification, a decision tree model is trained on the LLM's output (probabilities for each classification) along with other readily available features of the ticket, like length and sentiment. This hybrid approach leverages the LLM's initial analysis while allowing the decision tree to correct inaccuracies and improve overall classification performance, ultimately demonstrating how simpler models can bolster the effectiveness of flawed LLMs in practical applications.

  Doug, the author of the blog post "Coping with dumb LLMs using classic ML," explores the inherent unreliability of Large Language Models (LLMs) and proposes a method to mitigate their shortcomings by leveraging traditional machine learning techniques, specifically decision trees. He illustrates this concept with a practical example: determining whether a piece of text generated by an LLM constitutes a valid legal judgment.

  Doug begins by acknowledging the impressive capabilities of LLMs in generating human-like text, yet emphasizes their fundamental flaw: they lack true understanding and reasoning abilities. Consequently, while an LLM might produce text that superficially resembles a legal judgment, it may be nonsensical or contain critical errors upon closer inspection. This unreliability renders LLMs unsuitable for tasks requiring precise and logically sound outputs, such as drafting legal documents.

  To address this issue, Doug introduces the idea of employing a "judge" to evaluate the output of the LLM. This judge, rather than being a human expert, is implemented as a decision tree trained on a dataset of genuine and fabricated legal judgments. The decision tree learns to identify patterns and features that distinguish authentic judgments from the LLM-generated imitations. These features could include aspects like the structure of the text, the specific terminology used, the presence of citations, and the overall coherence of the arguments presented.

  The blog post details the process of training the decision tree using the scikit-learn library in Python. Doug meticulously explains the steps involved in preparing the dataset, selecting appropriate features, training the model, and evaluating its performance. He highlights the importance of using a balanced dataset containing both real and fake judgments to ensure the model learns to differentiate effectively between them.

  Doug further elaborates on the specific features used to train the decision tree. These include metrics like the frequency of certain keywords associated with legal language, the overall length of the document, and the complexity of the sentences used. He demonstrates how these features can be extracted from the text and used as input to the decision tree model.

  The results presented in the blog post demonstrate the effectiveness of this approach. The trained decision tree achieves a reasonable level of accuracy in distinguishing between genuine legal judgments and those generated by the LLM. While not perfect, the judge provides a significant improvement over relying solely on the LLM's output.

  Doug concludes by suggesting that this method can be generalized to other domains where the output of LLMs needs to be verified for accuracy and reliability. He argues that combining the generative power of LLMs with the discerning capabilities of classical machine learning models like decision trees offers a promising path towards harnessing the potential of LLMs while mitigating their inherent limitations. This hybrid approach allows for a more robust and trustworthy application of LLMs in various fields.

  • Large Language Models
  • LLMs
  • Classic Machine Learning
  • ML
  • artificial intelligence
  • AI
  • natural language processing
  • NLP
  • Decision Trees
  • Model Evaluation
  • Limitations of LLMs
  • Hybrid Approaches

  Summary of Comments ( 44 )
  https://news.ycombinator.com/item?id=42790820

  Verbose tl;dr

  Hacker News users discuss the practicality and limitations of the proposed decision-tree approach to mitigate LLM "hallucinations." Some express skepticism about its scalability and maintainability, particularly with the rapid advancement of LLMs, suggesting that improving prompt engineering or incorporating retrieval mechanisms might be more effective. Others highlight the potential value of the decision tree for specific, well-defined tasks where accuracy is paramount and the domain is limited. The discussion also touches on the trade-off between complexity and performance, and the importance of understanding the underlying limitations of LLMs rather than relying on patches. A few commenters note the similarity to older expert systems and question if this represents a step back in AI development. Finally, some appreciate the author's honest exploration of alternative solutions, acknowledging that relying solely on improving LLM accuracy might not be the optimal path forward.

  The Hacker News post titled "Coping with dumb LLMs using classic ML" (linking to an article about using decision trees to augment LLMs) has generated a modest discussion with several insightful comments.

  One commenter points out that the approach described in the article, which involves using a decision tree to guide the LLM's output, isn't fundamentally different from prompt engineering. They argue that crafting a detailed prompt is essentially providing a structured set of rules, much like a decision tree. This comment highlights the blurred lines between different techniques for controlling LLM behavior, suggesting that "prompt engineering" might encompass a wider range of methods than typically assumed.

  Another commenter raises the question of maintainability. They acknowledge the potential benefits of using decision trees for specific tasks but express concern about the long-term implications of managing and updating these trees as requirements evolve. They suggest that the complexity of maintaining a decision tree could outweigh its advantages in certain dynamic environments.

  A further comment delves into the limitations of relying solely on the LLM's internal representations. The commenter argues that while LLMs can store and access a vast amount of information, they lack a reliable mechanism for consistently applying this knowledge in a structured manner. This comment reinforces the article's premise, suggesting that external structures like decision trees can help bridge this gap and improve the reliability of LLM outputs.

  Another commenter draws a parallel with older symbolic AI techniques. They suggest that the approach of using decision trees with LLMs represents a return to these earlier methods, combining the strengths of both symbolic and statistical AI. This comment frames the discussion within a broader historical context of AI research.

  Finally, a commenter questions the scalability of the proposed approach. They wonder how well the decision tree method would perform with more complex scenarios and larger datasets, expressing skepticism about its general applicability. This comment introduces an important consideration for practical implementations of the described technique.

  Overall, the comments on Hacker News provide a valuable critique and extension of the article's core ideas. They raise important questions about the practicality, maintainability, and broader implications of using decision trees to enhance LLM performance, offering a nuanced perspective on the potential and limitations of this hybrid approach.