# In[13]:
with Timer() as t_train:
xgb_hist_clf_pipeline = xgb.train(params, dtrain, num_boost_round=num_rounds)
with Timer() as t_test:
y_prob_xgb_hist = xgb_hist_clf_pipeline.predict(dtest)
# In[14]:
y_pred_xgb_hist = binarize_prediction(y_prob_xgb_hist)
# In[15]:
report_xgb_hist = classification_metrics_binary(y_test, y_pred_xgb_hist)
report2_xgb_hist = classification_metrics_binary_prob(y_test, y_prob_xgb_hist)
report_xgb_hist.update(report2_xgb_hist)
# In[16]:
results_dict['xgb_hist']={
'train_time': t_train.interval,
results_dict['xgb_hist']={
'train_time': t_train
}
# In[22]:
y_prob_xgb_hist, t_test = test_xgboost(xgb_hist_clf_pipeline, X_test, y_test)
# In[23]:
y_pred_xgb_hist = binarize_prediction(y_prob_xgb_hist)
# In[24]:
results_dict['xgb_hist']['test_time'] = t_test
# In[25]:
del xgb_hist_clf_pipeline
# Training LightGBM model