Alteryx Advanced - Machine Learning Part 5

The video tutorial explains the process of creating machine learning pipelines using Altrix's tools. It covers the selection of regression and classification tools based on the target variable type, and provides examples of building a linear regression pipeline with car price data and a classification pipeline with bank marketing data. The tutorial also discusses data preparation, model training, and evaluation techniques.

What is a modeling pipeline according to Altrix?

What are the two tools in the Intelligent suite that help train models?

When should you use the regression tool instead of the classification tool?

What is the purpose of the assisted modeling tool in a machine learning pipeline?

What is one hot encoding and why is it necessary in data preparation?

What are the three algorithms available for regression mentioned in the text?

What does the fit tool do in the context of a machine learning pipeline?

How does the XG Boost model differ from logistic regression in terms of accuracy?

What are the general parameters that can be configured for the XG Boost model?

What is the significance of the 'Y_predicted' output in the classification pipeline?