Understanding Decision Trees

Ramandeep Kaur explains how decision trees work:

Simply put, a decision tree is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.

It is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems and works for both categorical and continuous input and output variables. It is one of the most widely used and practical methods for Inductive Inference. (Inductive inference is the process of reaching a general conclusion from specific examples.)

Decision trees learn and train itself from given examples and predict for unseen examples.

Click through for an example of implementing the ID3 algorithm and generating a decision tree from a data set.

Related Posts

Probabilities And Poker

Steve Miller has a notebook on 5-card draw probabilities: The population of 5 card draw hands, consisting of 52 choose 5 or 2598960 elements, is pretty straightforward both mathematically and statistically. So of course ever the geek, I just had to attempt to show her how probability and statistics converge. In addition to explaining the […]

Read More

Combining Keras With Apache MXNet

Lai Wei, et al, show how to build a neural network in Keras 2 using MXNet as the engine: Distributed training with Keras 2 and MXNet This article shows how to install Keras-MXNet and demonstrates how to train a CNN and an RNN. If you tried distributed training with other deep learning engines before, you […]

Read More


August 2017
« Jul Sep »