Data | XGBoosting

Helpful examples for preparing data for XGBoost models.

Data preparation for XGBoost models generally involves cleaning and transforming raw data into a suitable format for training, including handling missing values, encoding categorical variables, scaling features, and splitting the data into training and validation sets to ensure the model can learn effectively and generalize well to new data.

Examples	Tags
Data Preparation for XGBoost	Data
Detecting and Handling Data Drift with XGBoost	Data Inference
Encode Categorical Features As Dummy Variables for XGBoost	Data Categorical
Feature Engineering for XGBoost	Data Feature Engineering
Float Input Features for XGBoost	Data
Impute Missing Input Values for XGBoost	Data Missing
Integer Input Features for XGBoost	Data
Label Encode Categorical Input Variables for XGBoost	Data Categorical
Label Encode Categorical Target Variable for XGBoost	Data Categorical
Missing Input Values With XGBoost	Data Missing
One-Hot Encode Categorical Features for XGBoost	Data Categorical
Ordinal Encode Categorical Features for XGBoost	Data Categorical
Removing Outliers from Training Data For XGBoost	Data Outliers
String Input Features for XGBoost	Data Categorical
Text Input Features for XGBoost	Data
Train an XGBoost Model on a CSV File	Train Data
Train an XGBoost Model on a Dataset Stored in Lists	Train Data
Train an XGBoost Model on a DMatrix With Native API	Train Data
Train an XGBoost Model on a NumPy Array	Train Data
Train an XGBoost Model on a Pandas DataFrame	Train Data
Train an XGBoost Model on an Excel File	Train Data
Train XGBoost with DMatrix External Memory	Train Data Dmatrix
Use XGBoost Feature Importance for Feature Selection	Importance Data Feature Selection
Use XGBoost Feature Importance for Incremental Feature Selection	Importance Data Feature Selection
What is a DMatrix in XGBoost	Dmatrix Data
What is a QuantileDMatrix in XGBoost	Dmatrix Data
Why Use A DMatrix in XGBoost	Dmatrix Data
XGBoost "sample_weight" to Bias Training Toward Recent Examples (Data Drift)	Train Data
XGBoost Add Lagged Input Variables for Time Series Forecasting	Data Time Series
XGBoost Add Rolling Mean To Time Series Data	Data Time Series
XGBoost Assumes Data is IID (i.i.d.)	Data
XGBoost Assumes Stationary Time Series Data	Data Time Series
XGBoost Convert DMatrix to NumPy Array	Dmatrix Data
XGBoost Convert DMatrix to Pandas DataFrame	Dmatrix Data
XGBoost Convert NumPy Array to DMatrix	Dmatrix Data
XGBoost Convert Pandas DataFrame to DMatrix	Dmatrix Data
XGBoost Convert Python List to DMatrix	Dmatrix Data
XGBoost Detrend Transform Time Series Data	Data Time Series
XGBoost Difference Transform Time Series Data	Data Time Series
XGBoost Don't Use One-Hot-Encoding	Data Categorical
XGBoost Drop Non-Predictive Input Features	Data Feature Selection
XGboost Feature Engineering Of Dates	Data Feature Engineering
XGBoost Feature Selection with RFE	Data Feature Selection
XGBoost for Imbalanced Classification with SMOTE	Train Data Imbalanced
XGBoost for the Abalone Age Dataset	Datasets Data
XGBoost for the Adult Dataset	Datasets Data
XGBoost for the Boston Housing Dataset	Datasets Data
XGBoost for the California Housing Dataset	Datasets Data
XGBoost for the Cleveland Heart Disease Dataset	Datasets Data
XGBoost for the Covertype Dataset	Datasets Data
XGBoost for the Diabetes Dataset	Datasets Data
XGBoost for the Glass Identification Dataset	Datasets Data
XGBoost for the Handwritten Digits Dataset	Datasets Data
XGBoost for the Higgs Boson Dataset	Datasets Data
XGBoost for the Horse Colic Dataset	Datasets Data
XGBoost for the Ionosphere Dataset	Datasets Data
XGBoost for the Iris Dataset	Datasets Data
XGBoost for the KDDCup99 Dataset	Datasets Data
XGBoost for the Linnerud Dataset	Datasets Data
XGBoost for the Pima Indians Diabetes Dataset	Datasets Data
XGBoost for the Sonar Dataset	Datasets Data
XGBoost for the Wheat Seeds Dataset	Datasets Data
XGBoost for the Wholesale Customers Dataset	Datasets Data
XGBoost for the Wine Dataset	Datasets Data
XGBoost for the Wisconsin Breast Cancer Dataset	Datasets Data
XGBoost Interpolate Missing Values For Time Series Data	Data Time Series Missing
XGBoost Load CSV File as DMatrix	Dmatrix Data
XGboost Min-Max Scaling Numerical Input Features	Data
XGBoost Model Performance Improves With More Data	Data Plot
XGBoost NaN Input Values (missing)	Data Missing
XGBoost Native Categorical Faster Than One Hot and Ordinal Encoding	Performance Data Categorical
XGboost Normalize Numerical Input Features	Data
XGBoost Performs Automatic Feature Selection	Data Feature Selection
XGboost Power Transform Numerical Input Features	Data
XGBoost Power Transform Time Series Data	Data Time Series
XGBoost Print Data in DMatrix	Dmatrix Data
XGBoost Remove Least Important Features	Data Importance Feature Selection
XGboost Remove Outliers With Elliptic Envelope Method	Data Outliers
XGboost Remove Outliers With IQR Statistical Method	Data Outliers
XGboost Remove Outliers With Isolation Forest	Data Outliers
XGboost Remove Outliers With Local Outlier Factor	Data Outliers
XGboost Remove Outliers With One-Class SVM	Data Outliers
XGboost Remove Outliers With Z-Score Statistical Method	Data Outliers
XGBoost Robust to Correlated Input Features (multi-collinearity)	Data Robustness
XGBoost Robust to Mislabeled Data (label noise)	Data Robustness
XGBoost Robust to More Features Than Examples (P>>N)	Data Robustness
XGBoost Robust to Outliers in Data	Data Robustness Outliers
XGBoost Robust to Redundant Input Features	Data Robustness
XGBoost Robust to Small Datasets	Data Robustness Regularization
XGBoost Seasonal Difference Transform Time Series Data	Data Time Series
XGboost Standardize Numerical Input Features	Data
XGBoost's Native Support for Categorical Features	Data Categorical