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Inference

Helpful examples for inference with fit XGBoost models.

Inference with an XGBoost model refers to the process of using the trained model to make predictions on new, unseen data by applying the learned patterns and relationships from the training phase to generate output values.

ExamplesTags
Check if XGBoost Is Overfitting
Check if XGBoost Is Underfitting
Deploy XGBoost Model As Service with FastAPI
Deploy XGBoost Model As Service with Flask
Detecting and Handling Data Drift with XGBoost
Fit Final XGBoost Model and Predict on Out-Of-Sample Data
Fit Final XGBoost Model With Early Stopping and Predict on Out-Of-Sample Data
Out-of-Bag (OOB) Estimates of Performance for XGBoost
Plot Calibration Curve with XGBoost
Plot Out-of-Bag (OOB) Error for XGBoost
Predict Calibrated Probabilities with XGBoost
Predict Class Labels with XGBoost
Predict Class Probabilities with XGBoost
Predict Integer Values with XGBoost Regression
Predict Numeric Values with XGBoost Regression
Predict with XGBoost's Native API
Predict with XGBoost's scikit-learn API
Thread-Safe Predictions with XGBoost
Update XGBoost Model With New Data Using Native API
XGBoost booster.predict() vs XGBClassifer.predict()
XGBoost booster.predict() vs XGBRegressor.predict()
XGBoost Configure "OMP_NUM_THREADS" for Inference
XGBoost Configure The Number of BLAS Threads
XGBoost Configure The Number of OpenMP Threads
XGBoost Convert Predicted Probabilties to Class Labels
XGBoost for Time Series Predict Multiple Time Steps
XGBoost for Time Series Predict One Time Step
XGBoost for Time Series Predict Out-Of-Sample
XGBoost Model Slicing
XGBoost Parallel Prediction With a Process Pool (multiprocessing)
XGBoost Parallel Prediction With a Process Pool and Shared Memory
XGBoost Parallel Prediction With a Thread Pool (threading)
XGBoost Plot Learning Curve
XGBoost Plot Validation Curve
XGBoost Releases GIL During Inference (prediction)