Train Regression Model in Shifu

In most case, Shifu is designed for 0-1 regression, including data binning, data normalization and variable selection. But we can also do Linear Regression using Shifu.

There are two ways to train regression model in Shifu.

Method 1: Make temporary 0-1 Tag

• Create a temporary 0-1 target column by using original target (you can decide how to do do that.)
• Run shifu stats, shifu norm, shifu varsel as normal
• After the ColumnConfig.json is generated, and final variables are selected, then change temporary target column to original target column, and remove tags in posTags and negTags
• Add OutputActivationFunc to ModelConfig.json -> train -> params. The value of OutputActivationFunc could be Linear|ReLU|LeakyReLU|Swish. Depends on what you need.
• Rerun shifu norm and shifu train step to build model

Method 2: Native

• Keep posTags and negTags empty in ModelConfig.json
• Use EqualTotal to do binning when run shifu stats
• Use ONEHOT or ZSCALE_ONEHOT to do data normalization
• Since IV/KS are all zeros, you can use SE to do variable selection. Or you can use shifu varsel -f <variables.names.file> to select variables manually
• Add OutputActivationFunc to ModelConfig.json -> train -> params. The value of OutputActivationFunc could be Linear|ReLU|LeakyReLU|Swish. Depends on what you need.
• Rerun shifu norm and shifu train step to build model