Explore the world of Random Decision Forests on Apache Spark in this 53-minute conference talk from GOTO Amsterdam 2015. Dive into machine learning concepts as Sean Owen, Director of Data Science at Cloudera, guides you through feature vectors, decision trees, and model interpretation. Learn how to build and evaluate decision trees using MLlib, understand the importance of impurity reduction in decision-making, and discover techniques for tuning hyperparameters. Delve into advanced topics such as creating diverse opinions through subsets of examples and features, culminating in an exploration of Random Decision Forests. Gain practical insights into Apache Spark's capabilities for data scientists, including its distributed nature, REPL environment, and Python APIs alongside native Scala support.
A Taste of Random Decision Forests on Apache Spark
Overview
Syllabus
Introduction
Feature Vectors in the Iris Data Set
Good Pet Data Set
Possible Decision Trees
Interpreting Models
Building a Decision Tree in MLlib
Evaluating a Decision Tree
Better Than Random Guessing?
Decisions Should Make Lower Impurity Subsets
Tuning Hyperparameters
How to Create a Crowd?
Trees See Subsets of Examples
Or Subsets of Features
Diversity of Opinion
Random Decision Forests
Taught by
GOTO Conferences
