Decision trees – the unreasonable power of nested decision rules (mlu-explain.github.io)

Article summary

The article explains the concept of Decision Trees, a supervised machine learning algorithm used for classification and regression problems. It describes how Decision Trees work by partitioning the feature space into regions according to a series of conditional rules. The article also discusses the limitations of Decision Trees, including their instability and tendency to overfit the data. The authors introduce the concept of entropy and information gain, which are used to train Decision Trees.

Main themes

• Decision Trees
• Machine Learning
• Entropy and Information Gain
• Overfitting and Instability
• Neural Networks
• Explainability

What commenters say

• Decision Trees are effective but can be unstable and prone to overfitting, which limits their usefulness in certain applications.
• Some commenters argue that Decision Trees are not as explainable as they are often claimed to be, especially when they become complex.
• Neural Networks can be viewed as large decision trees in disguise, and there is potential for compiling neural networks into decision trees.
• The use of opaque models like deep neural networks in physics research is a concern, as they may prioritize curve fitting over understanding causal mechanics.
• Decision Trees can be parallelized and processed efficiently, but their performance may not be as impressive as other algorithms like linear classifiers.
• The concept of decision trees predates kD-Trees, and both use recursive partitioning of the function domain.
• Boosted Decision Trees and Random Forests are related but distinct techniques, with different training processes and resulting models.
• The increasing reliance on deep neural networks in physics research may lead to a lack of understanding of the underlying mechanics, and a focus on empirical fit over causal explanation.