Phase5-withAllWhite

The fifth phase of our project. In this phase we are hoping to create a final classifier of PDF files. The final classifier will be based on the three previous machines in our project: image, text, and features. The process of the final machine will be the following:

• install: sudo pip3 install xgboost
• Extract all data needed for the three base machines (image, text, features) - this is done using classes, imported into as.py.
• Create base vectors for every sample (image, text, features)
• Run every base machine on the samples, and return the calssification of the sample by every machine.
• Create a vector for the boost algorithm from the base machines classifications for every sample.
• Run boost algorithm with RF on sample boost vectors.
• Return boost algorithm accuracy.

all.png:

• AdaBoostClassifier: 8322 - true, 417 - false, accuracy - 95.23%.
• AdaBoostRegressor: 8197 - true, 542 - false, accuracy - 93.8%.
• XGBClassifier: 8487 - true, 252- false, accuracy - 97.12%.
• XGBRegressor: 8335 - true, 404 - false, accuracy - 95.38%.
• Random Forest Classifier: 8414 - true, 325 - false, accuracy - 96.28%.
• Random Forest Regressor: 8414 - true, 325 - false, accuracy - 96.28%.