R Extension : Lasso and Elastic-Net Regularized Generalized Linear Models
GLMNET Fit a generalized linear model via penalized maximum likelihood. The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda. Can deal with all shapes of data, including very large sparse data matrices. Fits linear, logistic and multinomial, poisson, and Cox regression models.
Prerequisites
- R library (glmnet) needs to be installed on R configured for HANA
Configuration Panel
| Parameter | Description |
|---|---|
| Predictors | Independent Attributes that will be part of the model |
| Response | The Target Column |
| Response Type | The family response types are Gaussian, binomial, poisson , multinominal, cox, mgaussian |
| Elasticnet Mixing Parameter | The Elasticnet mixing parameter “alpha” , with values between 0 and 1. alpha=1 is the lasso penalty, and alpha=0 the ridge penalty |
| The Number of Lambda Values | The number of lambda values – default is 100 |
| Should Intercepts be fitted | Should intercept(s) be fitted (default=TRUE) or set to zero (FALSE) |
| Maximum Number of Passes | Maximum number of passes over the data for all lambda values; default is 10^5 |
The configuration panel is shown below:
The Component on successful execution also generates a visualisation to understand how well the predictive values are in comparison to the actual response variable values. Mean Average Error and Root Mean Square Error is also reported. For additional statistics to understand the model can be attained by connecting the Model Statistic Node to this component.
How to Import this component ?
Kindly find the GLMNET R Extension [ HANA] spar file which could be imported into SAP Predictive Analytics – Expert Analytics.
Check here how to import the spar file into the product.
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