BigQuery ML Classification and Regression (AutoML Tables)
Using this Block, you can integrate Looker with BigQuery ML and AutoML Tables to get the benefit of advanced analytics without needing to be an expert in data science. Start with your problem: What is the outcome you want to achieve? What kind of data is the target column? Depending on your answers, this Block will create an auto-classification or auto-regression model to solve your use case:
- A binary classification model predicts a binary outcome (one of two classes). Use this for yes or no questions, for example, predicting whether a customer will make a purchase.
- A multi-class classification model predicts one class from three or more discrete classes. Use this to categorize things, like segmenting defect types in a manufacturing process.
- A regression model predicts a continuous value. Use this to predict customer spend or future return rates.
This Block gives business users the ability to make predictions (categorical or numerical) from a new or existing Explore. Explores created with this Block can be used to create multiple classification and regression models, evaluate them, and access their predictions in dashboards or custom analyses.
Learn more in the associated [AutoML Tables Beginner's Guide] (https://cloud.google.com/automl-tables/docs/beginners-guide).
Step by Step instructions for implementation are in the [Block Readme] (https://github.com/looker/block-bqml-automl#readme)