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

Key Concepts of Convolutional Neural Networks

Srinija Sirobhushanam takes us through some of the key concepts around convolutional neural networks: How are convolution layer operations useful?CNN helps us look for specific localized image features like the edges in the image that we can use later in the network Initial layers to detect simple patterns, such as horizontal and vertical edges in […]

Read More

LSTM in Databricks

Vedant Jain shows us an example of solving a multivariate time series forecasting problem using LSTM networks: LSTM is a type of Recurrent Neural Network (RNN) that allows the network to retain long-term dependencies at a given time from many timesteps before. RNNs were designed to that effect using a simple feedback approach for neurons where the […]

Read More

Categories

August 2017
MTWTFSS
« Jul Sep »
 123456
78910111213
14151617181920
21222324252627
28293031