Feature Importance - Xgboost Typing CST Test
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Feature Importance — Xgboost Code
Trains a model and prints feature importances.
import xgboost as xgb
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
data = load_breast_cancer()
X_train,X_test,y_train,y_test = train_test_split(data.data,data.target,test_size=0.2,random_state=42)
model = xgb.XGBClassifier(eval_metric='logloss')
model.fit(X_train,y_train)
print('Feature Importances:',model.feature_importances_)Xgboost Language Guide
XGBoost (Extreme Gradient Boosting) is an optimized, scalable, and high-performance gradient boosting framework based on decision trees, widely used for supervised learning tasks including classification, regression, and ranking.
Primary Use Cases
- ▸Binary and multiclass classification
- ▸Regression tasks
- ▸Learning-to-rank applications
- ▸Feature importance analysis
- ▸Integration in ML pipelines for structured/tabular data
Notable Features
- ▸Gradient boosting with L1/L2 regularization
- ▸Parallelized tree construction
- ▸Supports missing value handling natively
- ▸Tree pruning via depth-wise or loss-guided strategies
- ▸Supports GPU acceleration and distributed training
Origin & Creator
XGBoost was developed by Tianqi Chen and collaborators in 2014 to provide an efficient and scalable gradient boosting library, optimized for performance and accuracy.
Industrial Note
XGBoost is heavily used in Kaggle competitions, finance, healthcare analytics, adtech, recommendation systems, and any scenario needing fast and accurate tree-based predictions.