Introducing XGBoost

Extreme Gradient Boosting with XGBoost

Sergey Fogelson

Head of Data Science, TelevisaUnivision

What is XGBoost?

Optimized gradient-boosting machine learning library
Originally written in C++
Has APIs in several languages:
- Python
- R
- Scala
- Julia
- Java

What makes XGBoost so popular?

Speed and performance
Core algorithm is parallelizable
Consistently outperforms single-algorithm methods
State-of-the-art performance in many ML tasks

Using XGBoost: a quick example

import xgboost as xgb
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

class_data = pd.read_csv("classification_data.csv")

X, y = class_data.iloc[:,:-1], class_data.iloc[:,-1]

X_train, X_test, y_train, y_test= train_test_split(X, y, 
        test_size=0.2, random_state=123)

xg_cl = xgb.XGBClassifier(objective='binary:logistic', 
        n_estimators=10, seed=123)

xg_cl.fit(X_train, y_train)

preds = xg_cl.predict(X_test)

accuracy = float(np.sum(preds==y_test))/y_test.shape[0]

print("accuracy: %f" % (accuracy))

accuracy: 0.78333

Let's begin using XGBoost!

Extreme Gradient Boosting with XGBoost