Complete SAS Programming Guide - Learn SAS and Become a Data Ninja - The Major Steps in Predictive Modeling

The video tutorial covers the three major steps in predictive modeling: algorithm selection, training, and prediction. It begins with choosing the right algorithm based on whether the data is supervised or unsupervised, and whether the dependent variable is continuous or categorical. The tutorial then explains the training process, where the model learns associations between predictor and response variables using historical data. Finally, it discusses prediction, where the model estimates outcomes for new data using the learned rules.

What is the first step in predictive modeling?

• Algorithm selection

• Model evaluation

• Data collection

• Feature scaling

In supervised learning, what type of variable is essential?

• Independent variable

• Continuous variable

• Categorical variable

• Dependent variable

Which algorithm is suitable for a binary categorical response variable?

• K-means clustering

• Simple linear regression

• Logistic regression

• Multiple regression

What is the main goal of the training process in predictive modeling?

• To deploy the model

• To collect more data

• To learn correlations between variables

• To visualize data

What is the purpose of the prediction or scoring step?

• To validate the model

• To estimate the dependent variable

• To collect new data

• To select features