Classification and regression overview

Binary classification models predict a binary outcome (one of two classes). Use this model type for yes or no questions. For example, you might want to build a binary classification model to predict whether a customer would buy a subscription. Generally, a binary classification problem requires less data than other model types.

Multi-class classification models predict one class from three or more discrete classes. Use this model type for categorization. For example, as a retailer, you might want to build a multi-class classification model to segment customers into different personas.

Regression models predict a continuous value. For example, as a retailer, you might want to build a regression model to predict how much a customer will spend next month.

Workflow for creating a classification or regression model and making inferences

The process for creating a classification or regression model in Vertex AI is as follows:

Steps Description
1. Prepare training data Prepare your training data for model training.
2. Create a dataset Create a new dataset and associate your prepared training data with it.
3. Train a model Train a classification or regression model in Vertex AI using your dataset.
4. Evaluate your model Evaluate your newly trained model for inference accuracy.
5. View model architecture View the hyperparameter logs of the tuning trials and the hyperparameter logs of the final model.
6. Get inferences from your model

To get real-time inferences, deploy your model and get online inferences.

If you don't need real-time inferences, make batch inference requests directly to your model.