XG Boost
In this exercise, you'll practice yet another boosting technique. Dubbed the new queen of Machine Learning, XGBoost is an optimized distributed gradient boosting package that is "taking over the world!". That said, getting asked about it in a Machine Learning interview is likely or, at the very least, would be to your benefit to discuss in one of your answers to display your knowledge of cutting-edge and highly accurate algorithms.
The argument learning_rate=0.1
specifies the size of the step to take in each iteration while searching for the global minimum and max_depth
controls the size (depth) of the decision trees, here 3.
All relevant packages have been imported for you:
pandas
as pd
, train_test_split
from sklearn.model_selection
, accuracy_score
from sklearn.linear_model
, LogisticRegression
from sklearn.linear_model
, BaggingClassifier
and AdaBoostClassifier
from sklearn.ensemble
, and XGBClassifier
from xgboost
.
The loan_data
DataFrame is already split into X_train
, X_test
, y_train
and y_test
.
This exercise is part of the course
Practicing Machine Learning Interview Questions in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Instantiate
xgb = ____(____=____, random_state=123, learning_rate=0.1, max_depth=3)