Publisher's Synopsis
Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. If you are someone who learns by playing with the code and editing the data or equations to see what changes, then use those resources along with the book for a deeper understanding. The topics covered in this book are:
-An overview of decision trees and random forests-A manual example of how a human would classify a dataset, compared to how a decision tree would work
-How a decision tree works, and why it is prone to overfitting
-How decision trees get combined to form a random forest
-How to use that random forest to classify data and make predictions
-How to determine how many trees to use in a random forest
-Just where does the "randomness" come from
-Out of Bag Errors & Cross-Validation - how good of a fit did the machine learning algorithm make?
-Gini Criteria & Entropy Criteria - how to tell which split on a decision tree is best among many possible choices
-And More
