gistfile1.txt

bst0 = xgb.train(data = dtrain_0, max.depth = 9, eta = .01,sub_sample = .9, nround = 3000, objective = "binary:logistic")
bst1 = xgb.train(data = dtrain_1, max.depth = 9, eta = .01,sub_sample = .9, nround = 3000, objective = "binary:logistic")
bst2 = xgb.train(data = dtrain_2, max.depth = 9, eta = .01,sub_sample = .9, nround = 3000, objective = "binary:logistic")
bst3 = xgb.train(data = dtrain_3, max.depth = 9, eta = .01,sub_sample = .9, nround = 3000, objective = "binary:logistic")

#Predicting
pred_gb_pred_0 = predict(bst0, newdata=test_m_0)
pred_gb_pred_1 = predict(bst1, newdata=test_m_1)
pred_gb_pred_2 = predict(bst2, newdata=test_m_2)
pred_gb_pred_3 = predict(bst3, newdata=test_m_3)


gb_prediction_0 <- ifelse(pred_gb_pred_0 > 0.5, 's', 'b')
gb_prediction_1 <- ifelse(pred_gb_pred_1 > 0.5, 's', 'b')
gb_prediction_2 <- ifelse(pred_gb_pred_2 > 0.5, 's', 'b')
gb_prediction_3 <- ifelse(pred_gb_pred_3 > 0.5, 's', 'b')