Decision Trees


A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.

Overview

A Visual Introduction to Machine Learning
In machine learning, computers apply statistical learning techniques to automatically identify patterns in data.
decision-trees random-forests r2d3 decision-tree

Tutorials

Decision Trees in Machine Learning
A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression.
decision-trees random-forests decision-tree tutorial

Libraries

General
Scikit-learn
Examples for all the different utilities within scikit-learn.
scikit-learn naive-bayes linear-regression logistic-regression
Neural-Backed Decision Trees
Combine interpretability of a decision tree with accuracy of a neural network.
decision-trees neural-networks deep-learning interpretability
CatBoost
A high-performance open source library for gradient boosting on decision trees
gradient-boosting decision-trees catboost library
LightGBM - Light Gradient Boosting Machine
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms.
gradient-boosting decision-trees lightgbm library
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