In this webinar we'll introduce you to two tree-based machine learning algorithms, CART decision trees and RandomForests. Both of these
methods can be used for either regression or classification (i.e. Y = “Application Denied” or “Application Accepted”) and we will focus on classification in this presentation. We will discuss the advantages of tree-based techniques including their ability
to automatically handle variable selection, variable interactions, nonlinear relationships, outliers, and missing values. We'll explore the CART algorithm, bootstrap sampling, and the Random Forest algorithm (all with animations) and compare their predictive
performance using a real world dataset.
Who should attend:
·Attend if you want to implement data science techniques even without a data science, programming, or even an advanced statistical
you want to understand why data science techniques are so important for analysts.
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