In this webinar we'll introduce you to a powerful tree-based machine learning algorithm called gradient boosting. Gradient boosting often outperforms linear regression, Random Forests, and CART. Boosted trees
automatically handle variable selection, variable interactions, nonlinear relationships, outliers, and missing values.
We'll see that CART decision trees are the foundation of gradient boosting and discuss some of the advantages of boosting versus a Random Forest. We will explore the gradient boosting algorithm and discuss
the most important modeling parameters like the learning rate, number of terminal nodes, number of trees, loss functions, and more. We will demonstrate using an implementation of gradient boosting (TreeNet® Software) to fit the model and compare the performance
to a linear regression model, a CART tree, and a Random Forest.
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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