Summary
In this chapter, we implemented a binary logistic regression model. We introduced the business scenario based on the data collected by the city of Chicago about taxi services. After that, we learned how the binary logistic regression technique can be used to predict binary values.
In order to build an effective model, we performed a detailed analysis of the data, and then segmented the dataset according to our needs into three tables: one to host training data, the second for evaluation, and the last one to apply our classification model.
During the training phase of the BigQuery ML model, we constantly improved the performances of the ML model based on the confusion matrix and the ROC AUC value.
After that, we evaluated the best ML model on a new set of records to verify the absence of overfitting and gain more confidence in the good quality of our binary logistic regression model.
Finally, we applied our ML model to the last subset of records to predict the probability...
