日本語
At DataRobot, Multilabel modelling is a kind of classification task that, while similar to multiclass modelling, provides more flexibility. In multi-label modelling, each row in a dataset is associated with one or several labels. Extending this framework in our unlimited label mode and paired with feature discovery, allow the user frame a model that can be used to serve recommendations. Given the use-case, this recommendation model can provide rank ordered suggestions of content, product, or services that a specific customer might like.
As an example, if we have historic purchases of a sample of customers, we can look at common spending habits across demographics and shopping baskets, identify new features and able to rank order anticipated items at the customer level. Some of the features automatically generated might be most common category of item per a specific geography, or the degree of a customers proclivity to try new things.
About this Accelerator
The accelerator provided in this notebook trains a model on historical customer purchases in order to make recommendations for future visits. The DataRobot features that will be utilized in this notebook are Multi-Label modelling and feature discovery. Together the resulting model can provide rank ordered suggestions of content, product, or services that a specific customer might like.
What you will learn
- Analyze the datasets required
- Create a multi-label dataset for training
- Connect to DataRobot
- Configure a feature discovery project
- Generate features and models
- Generate recommendations for new visits
