Day #96 - Mean Encoding - Extensions and Generalizations

• Compact transformation of categorical variables
• Powerful basis of feature engineering

Using target variable in different tasks. Regression, Multi-class

• More stats - Percentiles, std, distribution bins
• Introducing new information from one vs all classifiers in multi-class tasks (N Different encodings)

Domains with many-to-many relationships

• User to Apps relationships
• Row for user-app relationship
• Vector for each app`

Time-series

• Presence of mean prev da, prev week, prev day
• Based on data create more complicated features

Encoding interactions and numerical features

• model structure, analyzing trees
• Extract from decision trees (If they are in neighboring nodes)
• xgboost, row features
• Use split points to identify new features
• Manually add more mean encoded interactions
• Involving categorical variables evaluate variable interactions

Correct validation reminder
Local experiments

• Estimate encodings on X_tr
• Map them to X_tr and X_val
• Regularize on X_tr
• Validate mode on X_tr / X_val split

Submission

• Estimate Encoding on whole Train data
• Map them to Train and Test
• Regularize on Train
• Fit on Train

Happy Learning!!!