CS-GY 6923: Machine Learning
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  • Introduction
  • What is Machine Learning?
  • Types of Machine Learning
    • Supervised Learning
      • Notations
      • Probabilistic Modeling
        • Naive Bayes Classifier
      • Linear Regression
      • Nearest Neighbor
      • Evaluating a Classifier
      • Parametric Estimation
        • Bayesian Approach to Parameter Estimation
        • Parametric Estimation for Simple Linear Regression
        • Parametric Estimation for Multivariate Linear Regression
        • Parametric Estimation for Simple Polynomial Regression
        • Parametric Estimation for Multivariate Polynomial Regression
      • Bias and Variance of an Estimator
      • Bias and Variance of a Regression Algorithm
        • Model Selection
      • Logistic Regression
      • Decision Trees
        • Using Decision Trees for Regression
        • Bias and Variance
      • Dimensionality Reduction
      • Neural Networks
        • Training a Neuron
        • MLP
          • Regression with Multiple Outputs
          • Advice/Tricks and Issues to Train a Neural Network
        • Deep Learning
      • Support Vector Machines
      • Ensemble Learning
    • Unsupervised Learning
      • K-Means Clustering
      • Probabilistic Clustering
    • Reinforcement Learning
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  1. Types of Machine Learning
  2. Supervised Learning
  3. Decision Trees

Using Decision Trees for Regression

In most cases where we use a Decision Tree for regression, the leaf nodes will now have means instead of labels. In some (rare) cases, we can also end up with linear equations in the leaf nodes, similar to the ones we obtain by linear regression.

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Last updated 5 years ago

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