This chapter will introduce you to the fundamental idea behind XGBoost—boosted learners. Once you understand how XGBoost works, you'll apply it to solve a common classification problem found in industry - predicting whether a customer will stop being a customer at some point in the future. This is the Summary of lecture "Extreme Gradient Boosting with XGBoost", via datacamp.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import xgboost as xgb
plt.rcParams['figure.figsize'] = (10, 5)
It's time to create your first XGBoost model! As Sergey showed you in the video, you can use the scikit-learn .fit() / .predict() paradigm that you are already familiar to build your XGBoost models, as the xgboost library has a scikit-learn compatible API!
Here, you'll be working with churn data. This dataset contains imaginary data from a ride-sharing app with user behaviors over their first month of app usage in a set of imaginary cities as well as whether they used the service 5 months after sign-up.
Your goal is to use the first month's worth of data to predict whether the app's users will remain users of the service at the 5 month mark. This is a typical setup for a churn prediction problem. To do this, you'll split the data into training and test sets, fit a small xgboost model on the training set, and evaluate its performance on the test set by computing its accuracy.
churn_data = pd.read_csv('./dataset/churn_data.csv')
from sklearn.model_selection import train_test_split
# Create arrays for the features and the target: X, y
X, y = churn_data.iloc[:, :-1], churn_data.iloc[:, -1]
# Create the training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
# Instantiate the XGBClassifier: xg_cl
xg_cl = xgb.XGBClassifier(objective='binary:logistic', n_estimators=10, seed=123)
# Fit the classifier to the training set
xg_cl.fit(X_train, y_train)
# Predict the labels of the test set: preds
preds = xg_cl.predict(X_test)
# Compute the accuracy: accuracy
accuracy = float(np.sum(preds == y_test)) / y_test.shape[0]
print("accuracy: %f" % (accuracy))
accuracy: 0.758200
Your task in this exercise is to make a simple decision tree using scikit-learn's DecisionTreeClassifier on the breast cancer dataset.
This dataset contains numeric measurements of various dimensions of individual tumors (such as perimeter and texture) from breast biopsies and a single outcome value (the tumor is either malignant, or benign).
We've preloaded the dataset of samples (measurements) into X and the target values per tumor into y. Now, you have to split the complete dataset into training and testing sets, and then train a DecisionTreeClassifier. You'll specify a parameter called max_depth. Many other parameters can be modified within this model, and you can check all of them out here.
X = pd.read_csv('./dataset/xgb_breast_X.csv').to_numpy()
y = pd.read_csv('./dataset/xgb_breast_y.csv').to_numpy().ravel()
from sklearn.tree import DecisionTreeClassifier
# Create the training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
# Instantiate the classifier: dt_clf_4
dt_clf_4 = DecisionTreeClassifier(max_depth=4)
# Fit the classifier to the training set
dt_clf_4.fit(X_train, y_train)
# Predict the labels of the test set: y_pred_4
y_pred_4 = dt_clf_4.predict(X_test)
# Compute the accuracy of the predictions: accuracy
accuracy = float(np.sum(y_pred_4 == y_test)) / y_test.shape[0]
print("Accuracy:", accuracy)
Accuracy: 0.9649122807017544
You'll now practice using XGBoost's learning API through its baked in cross-validation capabilities. As Sergey discussed in the previous video, XGBoost gets its lauded performance and efficiency gains by utilizing its own optimized data structure for datasets called a DMatrix.
In the previous exercise, the input datasets were converted into DMatrix data on the fly, but when you use the xgboost cv object, you have to first explicitly convert your data into a DMatrix. So, that's what you will do here before running cross-validation on churn_data.
churn_data = pd.read_csv('./dataset/churn_data.csv')
# Create arrays for the features and the target: X, y
X, y = churn_data.iloc[:, :-1], churn_data.iloc[:, -1]
# Create the DMatrix from X and y: churn_dmatrix
churn_dmatrix = xgb.DMatrix(data=X, label=y)
# Create the parameter dictionary: params
params = {'objective':"reg:logistic", "max_depth":3}
# Perform cross-validation: cv_results
cv_results = xgb.cv(dtrain=churn_dmatrix, params=params,
nfold=3, num_boost_round=5,
metrics="error", as_pandas=True, seed=123)
# Pint cv_results
print(cv_results)
# Print the accuracy
print(((1 - cv_results['test-error-mean']).iloc[-1]))
train-error-mean train-error-std test-error-mean test-error-std 0 0.28232 0.002366 0.28378 0.001932 1 0.26951 0.001855 0.27190 0.001932 2 0.25605 0.003213 0.25798 0.003963 3 0.25090 0.001845 0.25434 0.003827 4 0.24654 0.001981 0.24852 0.000934 0.75148
cv_results stores the training and test mean and standard deviation of the error per boosting round (tree built) as a DataFrame. From cv_results, the final round 'test-error-mean' is extracted and converted into an accuracy, where accuracy is 1-error. The final accuracy of around 75% is an improvement from earlier!
Now that you've used cross-validation to compute average out-of-sample accuracy (after converting from an error), it's very easy to compute any other metric you might be interested in. All you have to do is pass it (or a list of metrics) in as an argument to the metrics parameter of xgb.cv().
Your job in this exercise is to compute another common metric used in binary classification - the area under the curve ("auc").
# Perform cross_validation: cv_results
cv_results = xgb.cv(dtrain=churn_dmatrix, params=params,
nfold=3, num_boost_round=5,
metrics="auc", as_pandas=True, seed=123)
# Print cv_results
print(cv_results)
# Print the AUC
print((cv_results["test-auc-mean"]).iloc[-1])
train-auc-mean train-auc-std test-auc-mean test-auc-std 0 0.768893 0.001544 0.767863 0.002820 1 0.790864 0.006758 0.789157 0.006846 2 0.815872 0.003900 0.814476 0.005997 3 0.822959 0.002018 0.821682 0.003912 4 0.827528 0.000769 0.826191 0.001937 0.826191
An AUC of 0.84 is quite strong. As you have seen, XGBoost's learning API makes it very easy to compute any metric you may be interested in. In Chapter 3, you'll learn about techniques to fine-tune your XGBoost models to improve their performance even further. For now, it's time to learn a little about exactly when to use XGBoost.