Managing unseen categorical levels in predictive modeling with mlr3
I have trained a glmnet model using data that includes a categorical variable as a predictor, with the mlr3 package.
I am trying to make predictions using the glmnet model on new data.
Managing unseen categorical levels in predictive modeling with mlr3
I have trained a glmnet model using data that includes a categorical variable as a predictor, with the mlr3 package.
I am trying to make predictions using the glmnet model on new data.
mlr3: Error Accessing Model Coefficients in lrn(“surv.penalized”)
I encountered an error when trying to access model coefficients using mlr3. Can you help me understand how to do this properly?
mlr3: Extracting non-zero coefficient in lrn(“surv.penalized”)
I’m doing an ML study. I’ve used surv.penalized for lasso Penalized Cox Regression Learner, and get the non-zero coefficients. And it seems to work i.e. I do get some non-zero coefficients and the rest go to zero. Is there a way to extract coefficients and their names that are not zero?
Error message in parameter tuning: OptimInstanceMultiCrit is deprecated. Use OptimInstanceBatchMultiCrit instead
I have an error message when I run a tuning process:
Tuning Over Multiple Learners using mutations and target transformations via pipelines
Next to a mutation, I want to transform the target variable through a pipeline.
mlr3: How to extract predicted survival time? (to compare the model predictions with the real data)
I want to predict the response of predicted survival times in survival analysis. According to the book mlr3 for comparing the predictions from the model to the true data (https://mlr3book.mlr-org.com/chapters/chapter13/beyond_regression_and_classification.html#learnersurv-predictionsurv-and-predict-types).