Wrangling data with good defaults

This section covers how to build a predictive pipeline starting from a dataframe. The skrub objects described in this section can be used as strong defaults for building baseline pipelines, and can be customized for specific use cases.

• Cleaner: sanitizing a dataframe
  • Converting numeric dtypes to float32 with the Cleaner
• Transforming a table into an array of numeric features: TableVectorizer
• Building robust ML baselines with tabular_pipeline()