Update training.R

training.R

@@ -1,34 +1,19 @@
 train_save_model <- function(cleaned_df, outcome_df) {
+  # Trains a model using the cleaned dataframe and saves the model to a file.
 
+  # Parameters:
+  # cleaned_df (dataframe): The cleaned data from clean_df function to be used for training the model.
+  # outcome_df (dataframe): The data with the outcome variable (e.g., from PreFer_train_outcome.csv or PreFer_fake_outcome.csv).
 
-  # Combine cleaned_df and outcome_df to match on ID
-  model_df <- merge(cleaned_df, outcome_df, by = "nomem_encr")
-
-  # glmnet requires matrix, merge turned it into data.frame
-  model_df <- as.matrix(model_df)
-
-  # features without outcome and identifier
-  X <- model_df[ , !(colnames(model_df) %in% c("nomem_encr", "new_child"))]
-  # outcome only
-  y <- model_df[ , colnames(model_df) == "new_child"]
+  ## This script contains a bare minimum working example
+  set.seed(1) # not useful here because logistic regression deterministic
 
-  # LASSO regression
-  # cross-validation, to retrieve ideal lambda
-  # hyperparameter tuning
-  set.seed(1)
-  CV <- cv.glmnet(x = X, 
-                  y = y, 
-                  family = "binomial",
-                  nfolds = 10, standardize = FALSE)
-  optimal_lambda_test <- CV$lambda.min
+  # Combine cleaned_df and outcome_df
+  model_df <- merge(cleaned_df, outcome_df, by = "nomem_encr")
 
-  # Run model with optimal lambda
-  model <- glmnet(x = X, 
-                  y = y, 
-                  family = "binomial", 
-                  lambda = optimal_lambda_test, standardize = FALSE )
+  # Logistic regression model
+  model <- glm(new_child ~ age, family = "binomial", data = model_df)
 
   # Save the model
   saveRDS(model, "model.rds")
-
-}
+}