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. |