tokens: the test set, where the training set builds the model, and the testing set evaluates the model's performance. In principle, the predictor performs better with a larger fraction of training data. On the other hand, the performance statistic will have greater variance if there is less testing data (Kohavi 1995 ). To strike a balance, we have tried changing the ratios of our training and testing sets from 50:50 to 90:10, and we verify that the XGBoost performs the best in both AUROCC and f1-score with a 70:30 split. To avoid over-fitting, we adopt an early stopping in XGBoost after five training iterations that do not yield any improvements. To control the balance of positive and negative weights, we set the scale _ pos _ weight = 28.7, which is given by the ratio between the number of negative and positive instances (4705/164; see Section 2.3 ). 3.3. Feature Selection Feature selection is an important process in machine learning that strongly influences the performance of the model. Feature selection is a procedure of selecting a subset of relevant features for model construction. In general, proper feature selection can increase the efficiency by reducing the training duration, enhancing generalization by reducing over-fitting, and improving the prediction performance (see Chen Guestrin 2016 ; An et al. 2019 ). A trained XGBoost model will automatically calculate feature importance and provide the feature importance scores. In this work, we first trained the XGBoost model based on the training data set and selected features by sorting feature importance scores calculated from the trained model. We then iteratively trained the model based on the selected subset of features until the point of best performance. We verified that both f1-score and AUROCC increase with the number of selected features until we use up all 79 features. Therefore, we did not reduce the number of feature vectors in this work. Considering the limitations of our computational resources, we repeated this procedure 10 times by using a different combination of training and test data sets in each iteration and estimated the average feature importance score from these 10 realizations. The top five important features in our sample are: K , ([3.6]−[4.5]), ( K − [4.5]), ([3.6]–[5.8]), and ( H − K ), which are similar to those photometric wave bands used by Chen et al. ( 2016 ; Optical-Infrared Triple Color: ( z − K ), ( K − [3.6]), and ([3.6]–[4.5])) and An et al. ( 2019 ; ( z − K ), ( J − K ), ( K − [3.6]), and ([3.6]–[4.5])). The top five important features in our sample can be associated to fundamental physical properties. The K -band flux roughly maps to the stellar mass, while the SMGs appear to be red and occupy a relatively well-defined region in near-infrared color–color space (see also Chen et al. 2016 ; An et al. 2019 ). 3.4. Tuning the Hyper-parameters The hyper-parameters are a set of parameters that define the machine-learning algorithm as a mathematical formula. The
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