Use Case Documentation
Predictive Modeling

Choose this use-case if you wish to develop a classification or regression model that analyzes historical data to aid in trend forecasting, risk identification, resource allocation, content personalization, or strategy. Given a dataset containing input features and a target variable, you can generate a model that predicts values for your target variable. Features can be numerical (e.g., age, salary), categorical (e.g., gender, city), ordinal (e.g., ratings on a scale of 1-5), or binary (e.g., yes/no answers.) The target variable is categorical in a classification problem and numerical in a regression problem.

Dataset and Feature Group Requirements

This section specifies the Datasets / Feature Groups requirements to successfully train a Predictive Modeling model. Feature requirements include recommendations on additional datasets that might enhance model performance.

Training Models - Training Options and Metrics

This section describes all the available model training options that can be used to create a Predictive Modeling model. You can utilize the metric explanations to better understand how they measure the performance of the model you trained.

Evaluating Predictions

This section contains a quick model evaluation guide that helps you understand how well your model is performing.

Prediction API

This section discusses the prediction API method so that you could properly generate predictions from the model you deployed.