TD_NonLinearCombineFit Function | NonLinearCombineFit - TD_NonLinearCombineFit - Teradata Vantage

Feature engineering is the process of creating features from existing data to improve the performance of a machine learning model. One approach to feature engineering is to use a technique that combines existing features in a non-linear way to create ones that capture more complex relationships between the original features and the target.

By combining features in a non-linear way, this technique can create features that are not explicitly present in the original dataset. These features can help to simplify the model, reduce overfitting, and improve the interpretability of the model. Moreover, this technique can help to identify the most relevant features by evaluating the performance of the model with different combinations of the original features and their non-linear combinations.

In summary, feature engineering with non-linear combinations of features is an important technique for improving the performance and interpretability of machine learning models. This can help to create features, simplify the model, reduce overfitting, and identify the most relevant features for the target.

Function Information

• TD_NonLinearCombineFit Syntax
• Required Syntax Elements for TD_NonLinearCombineFit
• Optional Syntax Elements for TD_NonLinearCombineFit
• TD_NonLinearCombineFit Input
• TD_NonLinearCombineFit Output
• Example: How to Use TD_NonLinearCombineFit