I am running a Lasso regression for a model with one target and several predictors. I have standardized the predictors (but not the target) before running the regression. The results I am getting confirm my notion of the phenomenon, and Lasso is doing a good job of reducing the co-efficient of predictors which are not (or should not be) important to 0.

My question is how do I use the Lasso co-efficients for predictions and reporting? Since I had standardized the predictor values, should I re-scale the non-zero co-efficients before making predictions and reporting the regression equation?

P.S. I am running the model on data from a phenomenon I know well to get a good understanding of 'shrinkage' regression models

I am running a Lasso regression for a model with one target and several predictors. I have standardized the predictors (but not the target) before running the regression. The results I am getting confirm my notion of the phenomenon, and Lasso is doing a good job of reducing the coefficients of predictors which are not (or should not be) important to $0$.

My question is: How do I use the Lasso coefficients for predictions and reporting? Since I had standardized the predictor values, should I re-scale the non-zero coefficients before making predictions and reporting the regression equation?

P.S. I am running the model on data from a phenomenon I know well to get a good understanding of 'shrinkage' regression models.

I am running a Lasso regression for a model with one target and several predictors. I have standardized the predictors (but not the target) before running the regression. The results I am getting confirm my notion of the phenomenon, and Lasso is doing a good job of reducing the co-efficient of predictors which are not (or should not be) important to 0.

My question is how do I use the Lasso co-efficients for predictions and reporting? Since I had standardized the predictor values, should I re-scale the non-zero co-efficients before making predictions and reporting the regression equation?

P.S. I am running the model on a data and phenomenon I know well to get a good understanding of 'shrinkage' regression models

I am running a Lasso regression for a model with one target and several predictors. I have standardized the predictors (but not the target) before running the regression. The results I am getting confirm my notion of the phenomenon, and Lasso is doing a good job of reducing the co-efficient of predictors which are not (or should not be) important to 0.

My question is how do I use the Lasso co-efficients for predictions and reporting? Since I had standardized the predictor values, should I re-scale the non-zero co-efficients before making predictions and reporting the regression equation?

P.S. I am running the model on data from a phenomenon I know well to get a good understanding of 'shrinkage' regression models