Fine-tuning your AutoML classification model
In this section, you will first review tips and tricks for improving your AutoML classification models and then review the algorithms used by AutoML for both binary and multiclass classification.
Improving AutoML classification models
Keeping in mind the tips and tricks from Chapter 4, Building an AutoML Regression Solution, here are new ones that are specific to classification:
- Unlike regression problems, nearly all classification problems in the real world require you to weigh your target column. The reason is that, for most business problems, one class is nearly always more important than the others.
For example, imagine you are running a business and you are trying to predict which customers will stop doing business with you and leave you for a competitor. This is a common problem called customer churn or customer turnover. If you misidentify a customer as being likely to churn, all you waste is an unnecessary phone call...
