matching

Scripts/logit/chr_vol_treat.R

 library(dplyr)
 library(tidyr)
 library(margins)
-<<<<<<< HEAD
 library(lubridate)
 library(caret)
 library(randomForest)
-=======
 library(pROC)
->>>>>>> e410a7a5c52ecf34b454cff74f0b641cd5615679
 
 library(xgboost)
 # Test treatment effect
@@ -67,7 +64,7 @@ data$predicted.classes <- ifelse(data$probabilities  >= cut_off, 1, 0)
 # Model accuracy
 mean(data$predicted.classes == data$Choice_Treat, na.rm = T)
 
-<<<<<<< HEAD
+
 
 
 
@@ -145,7 +142,8 @@ table(labeled_data$Choice_Treat, labeled_data$PredictedGroup)
 unlabeled_predictions <- predict(model3, newdata = unlabeled_data)
 unlabeled_data$PredictedGroup <- unlabeled_predictions
 print(model3$bestTune)
-=======
+
+
 table(data$predicted.classes ,data$Choice_Treat)
 
 calculate_metrics <- function(confusion_matrix) {