Decision trees as base learners

It's now time to build an XGBoost model to predict house prices - not in Boston, Massachusetts, as you saw in the video, but in Ames, Iowa! This dataset of housing prices has been pre-loaded into a DataFrame called df. If you explore it in the Shell, you'll see that there are a variety of features about the house and its location in the city.

In this exercise, your goal is to use trees as base learners. By default, XGBoost uses trees as base learners, so you don't have to specify that you want to use trees here with booster="gbtree".

xgboost has been imported as xgb and the arrays for the features and the target are available in X and y, respectively.

This exercise is part of the course

Extreme Gradient Boosting with XGBoost

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Exercise instructions

Split df into training and testing sets, holding out 20% for testing. Use a random_state of 123.
Instantiate the XGBRegressor as xg_reg, using a seed of 123. Specify an objective of "reg:squarederror" and use 10 trees. Note: You don't have to specify booster="gbtree" as this is the default.
Fit xg_reg to the training data and predict the labels of the test set. Save the predictions in a variable called preds.
Compute the rmse using np.sqrt() and the mean_squared_error() function from sklearn.metrics, which has been pre-imported.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create the training and test sets
X_train, X_test, y_train, y_test = ____(____, ____, ____=____, random_state=123)

# Instantiate the XGBRegressor: xg_reg
xg_reg = ____

# Fit the regressor to the training set
____

# Predict the labels of the test set: preds
preds = ____

# Compute the rmse: rmse
rmse = ____(____(____, ____))
print("RMSE: %f" % (rmse))

Edit and Run Code