XGBoost load model.py

#Create a classification model with set parameters, and then instead of fitting,
#load my model from the included JSON
classification_model = xgb.XGBClassifier(objective="binary:logistic", random_state=42, learning_rate = 0.05, n_estimators = 5000, early_stopping_rounds = 10)
classification_model.load_model('graph_classifier_3.json')

#Make a prediction
pred = classification_model.predict(embeddingsframe)
print(screen_name + ': ' + pred[0])