This is jaeyun's CVR Prediction Report.

0. Directory structure

CVR_Prediction
└code
    └model
        └model.py
        └__init__.py
    └util
        └data_preprocessing.py
        └dataset.py
        └__init__.py
    └config.py
    └main.py
    └requirements.txt
└data
    └CriteoSearchData  #input data here 
└save
README.md

1. DataSet

[Download here]

2. Setting up environment

conda create -n test python=3.6
source activate test
pip install -r requirements.txt

3. Variable setting

- config.py 

      configure = {
      'DATA_PATH' : "$PATH/data/", ## data path
      'DATA_FILE' : "CriteoSearchData", ## data file
      'TRAIN_DATA' : [0,1000000], ## split train data range
      'TEST_DATA' : [1000000,1500000], ## split test data range
      'MODEL_SAVE_PATH' : "$PATH/save/", ## save path
      'MODEL_SAVE_FILE' : "models.dat", ## save model name
      'MODEL_LOAD_PATH' : "$PATH/save/", ## load path
      'MODEL_LOAD_FILE' : "models.dat", ## load model name
      'BATCH' : 100000, ## batch size
      'EPOCH' : 200, ## epoch
      'CUDA' : True, ## gpu use or not
      'SAVE' : True, ## model save or not
      'LOAD' : False, ## model load or not
      'TRAIN' : True ## train or not
}

4. Model training

# Change "LOAD" to False and "TRAIN" to True in the /CVR_Prediction/code/config.py file.
# location is /CVR_Prediction/code/
python main.py

5. Model evaluating

# Change "LOAD" to True and "TRAIN" to False in the /CVR_Prediction/code/config.py file.
# location is /CVR_Prediction/code/
python main.py

6. Result

(1) Evaluation index

I evaluated the model using accuracy. Accuracy = (True Positives + True Negatives) / (True Positivies + True Negatives + False Positives + False Negatives)

• True Positives : The model predicted to be 1, and label is 1.
• True Negatives : The model predicted to be 1, and label is 0.
• False Positivies : The model predicted to be 0, and label is 0.
• Flase Negatives : The model predicted to be 0, and label is 1.

(2) Accuracy

Train Data	Test Data
81.67%	82.09%

(3) Loss

Train Data	Test Data
0.4647	0.4580