Prediction (Supervised ML)¶
Prediction (aka supervised machine learning) is used when you have a target variable that you want to predict. For instance, you may want to predict the price of apartments in New York City using the size of the apartments, their location and amenities in the building. In this case, the price of the apartments is the target, while the size of the apartments, their location and the amenities are the features used for prediction.
Our Tutorial 103 provides a step-by-step explanation of how to create your first prediction model and deploy it for scoring of new records.
The rest of this document assumes that you have followed this tutorial.
Use the following steps to quickly start your first prediction model in DSS:
- Go to the Flow for your project
- Click on the dataset you want to use
- Select the Lab
- Select Quick model then Prediction
- Choose your target variable (one of the columns) and Automated Machine Learning
- Choose Quick Prototypes and click Create
- Click Train
- Prediction settings
- Target settings
- Settings: Train / Test set
- Settings: Metrics
- Settings: Features handling
- Settings: Feature generation
- Settings: Feature reduction
- Settings: Algorithms
- Settings: Hyperparameters optimization
- Settings: Metric
- Misc: GPU support for XGBoost
- Prediction Results