Topics: attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio from Marina Santini
Lecture 8: Decision Trees & k-Nearest Neighbors from Marina Santini Outline Decision trees, best split, entropy, information gain, gain ratio, k-Nearest Neighbors, distance metric. Course Website: http://stp.lingfil.uu.se/~santinim/ml/2014/ml4lt_2014.htm
The Weka workbench is a collection of state-of-the-art machine learning algorithms and data preprocessing tools. This presentation shows some basic features.
Lecture 02: Machine Learning for Language Technology – Decision Trees and Nearest Neighbors In this lecture, we talk about two different discriminative machine learning methods: decision trees and k-nearest neighbors. Decision trees are hierarchical structures. k-Nearest neighbors are based on…