{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-xgboost","slug":"pypi-xgboost","name":"xgboost","type":"repo","url":"https://pypi.org/project/xgboost/","page_url":"https://unfragile.ai/pypi-xgboost","categories":["model-training"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-xgboost__cap_0","uri":"capability://data.processing.analysis.gradient.boosted.tree.ensemble.training","name":"gradient-boosted-tree-ensemble-training","description":"Trains gradient boosted decision tree ensembles using a column-block sparse matrix format and level-wise tree growth strategy. XGBoost implements a custom tree-building algorithm that evaluates all possible splits in parallel across features, using weighted quantile sketching to handle large datasets that don't fit in memory. The framework supports both exact greedy splitting and approximate histogram-based splitting with configurable precision tradeoffs.","intents":["Train a high-performance gradient boosting model on tabular data with automatic feature interaction discovery","Build ensemble models that handle sparse, high-dimensional datasets efficiently","Optimize model training speed and memory usage for datasets with millions of rows and thousands of features"],"best_for":["Data scientists building production ML pipelines for tabular/structured data","Kaggle competitors and ML practitioners optimizing for predictive accuracy","Teams deploying models where inference speed and model interpretability matter"],"limitations":["Requires manual feature engineering — no automatic feature discovery like neural networks","Memory usage scales with dataset size; approximate splitting trades accuracy for speed on very large datasets","Tree depth and ensemble size must be tuned manually; no automatic architecture search","Single-machine training becomes bottleneck for datasets >100GB; distributed training requires additional setup"],"requires":["Python 3.7+","NumPy and SciPy for numerical operations","Pandas for DataFrame input (optional but recommended)","C++ compiler for building from source (pre-built wheels available)"],"input_types":["NumPy arrays (dense or sparse CSR/CSC format)","Pandas DataFrames","DMatrix objects (XGBoost's native format)","Sparse matrices (scipy.sparse)"],"output_types":["Trained Booster model object","Feature importance scores (gain, cover, frequency)","Tree structure metadata (for visualization/interpretation)"],"categories":["data-processing-analysis","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-xgboost__cap_1","uri":"capability://data.processing.analysis.batch.prediction.with.gpu.acceleration","name":"batch-prediction-with-gpu-acceleration","description":"Performs inference on trained models using GPU acceleration via CUDA/ROCm or CPU fallback, with support for batch prediction on large datasets. XGBoost's prediction engine loads the compiled tree ensemble into GPU memory and evaluates all samples in parallel across the tree structure, achieving 10-100x speedup over CPU inference depending on batch size and tree depth. Supports both single-sample and vectorized batch prediction with automatic device selection.","intents":["Generate predictions on large test datasets with minimal latency","Deploy models in production with GPU inference for real-time serving","Perform batch scoring on millions of samples efficiently"],"best_for":["Production ML systems requiring sub-millisecond latency predictions","Data science teams with GPU infrastructure (NVIDIA/AMD)","Batch processing pipelines scoring large datasets nightly"],"limitations":["GPU acceleration requires NVIDIA CUDA 10.0+ or AMD ROCm; CPU fallback available but slower","GPU memory limits batch size; very large datasets still require chunking","Prediction latency includes GPU transfer overhead (~1-5ms); beneficial only for batch sizes >100","GPU support only available in XGBoost 1.5+; older versions CPU-only"],"requires":["Python 3.7+","NVIDIA CUDA 10.0+ (for GPU acceleration) OR ROCm 3.5+ (for AMD GPUs)","Trained XGBoost Booster model","NumPy or Pandas for input data"],"input_types":["NumPy arrays","Pandas DataFrames","DMatrix objects","Sparse matrices"],"output_types":["NumPy arrays of predictions (regression/classification scores)","Probability arrays (for multi-class classification)","Leaf indices (for feature extraction)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-xgboost__cap_10","uri":"capability://data.processing.analysis.sample.weighting.and.class.balancing","name":"sample-weighting-and-class-balancing","description":"Assigns different weights to training samples, enabling handling of imbalanced datasets, cost-sensitive learning, and sample importance weighting. XGBoost's training loop incorporates sample weights into gradient/Hessian computation, allowing the model to focus on high-weight samples. Supports both per-sample weights (for importance weighting) and per-class weights (for class imbalance), with automatic weight normalization.","intents":["Handle imbalanced datasets where one class is much rarer than others","Implement cost-sensitive learning where misclassifying certain classes is more expensive","Weight samples by importance (e.g., recent samples more important than old samples)"],"best_for":["Data scientists working with imbalanced datasets (fraud detection, rare disease diagnosis)","Teams implementing cost-sensitive learning for business-critical applications","Practitioners with domain knowledge about sample importance"],"limitations":["Sample weights are heuristics; no principled way to set optimal weights","Class weights don't address root cause of imbalance; resampling or synthetic data generation may be better","Extreme weights can cause numerical instability; requires careful tuning","Weights don't affect feature importance computation; importance scores may be misleading"],"requires":["Python 3.7+","Sample weights (NumPy array, same length as training data)","XGBoost 0.90+"],"input_types":["Features (DMatrix or NumPy array)","Labels (NumPy array)","Sample weights (NumPy array, optional)"],"output_types":["Trained Booster model","Training history (weighted metrics)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-xgboost__cap_11","uri":"capability://memory.knowledge.tree.structure.visualization.and.export","name":"tree-structure-visualization-and-export","description":"Exports trained trees to human-readable formats (DOT, JSON, text) and visualizes tree structure for model interpretation. XGBoost's plot_tree() function renders individual trees as directed acyclic graphs showing split decisions, leaf values, and sample counts. Exported trees can be visualized in external tools (Graphviz) or analyzed programmatically, enabling debugging and understanding of model behavior.","intents":["Visualize individual decision trees to understand model logic and debug overfitting","Export trees for documentation, presentations, or regulatory compliance","Analyze tree structure to identify redundant or suspicious splits"],"best_for":["Data scientists debugging model behavior and validating feature interactions","Teams building interpretable ML systems for regulated industries","Practitioners presenting models to non-technical stakeholders"],"limitations":["Large trees (depth >10) are hard to visualize; require zooming or filtering","Tree visualization doesn't show feature interactions or global model behavior","Exporting all trees in large ensembles (1000+ trees) produces huge files","Visualization tools (Graphviz) must be installed separately"],"requires":["Python 3.7+","Trained XGBoost Booster model","Matplotlib (for plot_tree) or Graphviz (for external visualization)"],"input_types":["Trained Booster object","Tree index (which tree to visualize)"],"output_types":["Matplotlib figure (for plot_tree)","DOT format string (for Graphviz)","JSON representation of tree structure","Text representation of tree"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-xgboost__cap_2","uri":"capability://data.processing.analysis.feature.importance.extraction.and.analysis","name":"feature-importance-extraction-and-analysis","description":"Extracts multiple types of feature importance scores from trained tree ensembles: gain (average loss reduction per feature), cover (average number of samples affected), and frequency (number of times feature appears in splits). XGBoost traverses the compiled tree structure and aggregates statistics across all trees, supporting both global importance (across entire model) and per-tree importance for interpretability. Importance scores are normalized and can be exported for visualization or downstream analysis.","intents":["Understand which features drive model predictions for model debugging and validation","Identify and remove low-importance features to reduce model complexity and inference latency","Generate feature importance reports for stakeholders and regulatory compliance (SHAP-style explanations)"],"best_for":["Data scientists validating model behavior and feature engineering decisions","ML engineers optimizing models for production deployment (feature pruning)","Teams building interpretable ML systems for regulated industries"],"limitations":["Importance scores are model-centric, not data-centric; don't account for feature correlations or interactions","Gain-based importance biased toward high-cardinality features; frequency-based importance biased toward early splits","No built-in statistical significance testing; importance scores are relative, not absolute","Doesn't explain individual predictions — use SHAP or LIME for local explanations"],"requires":["Python 3.7+","Trained XGBoost Booster model","NumPy for numerical operations"],"input_types":["Trained Booster object"],"output_types":["Dictionary mapping feature names to importance scores","Pandas Series or DataFrame for visualization","JSON for export/logging"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-xgboost__cap_3","uri":"capability://data.processing.analysis.custom.objective.and.metric.functions","name":"custom-objective-and-metric-functions","description":"Allows users to define custom loss functions (objectives) and evaluation metrics via Python callbacks, enabling optimization for domain-specific tasks beyond standard classification/regression. XGBoost's training loop calls user-provided gradient/Hessian functions at each boosting iteration, allowing arbitrary differentiable objectives (e.g., custom ranking losses, fairness-constrained objectives). Custom metrics are evaluated on validation sets and used for early stopping without modifying core training logic.","intents":["Optimize models for custom business metrics (e.g., profit, AUC at specific operating points) instead of standard loss functions","Implement fairness constraints or domain-specific objectives (e.g., ranking, survival analysis)","Integrate XGBoost into specialized ML pipelines with non-standard evaluation criteria"],"best_for":["ML practitioners with domain-specific optimization requirements (finance, healthcare, ranking)","Teams building fairness-aware ML systems with custom constraint objectives","Researchers experimenting with novel loss functions and training objectives"],"limitations":["Custom objectives must be twice-differentiable; non-smooth functions require approximation","Gradient/Hessian computation is user's responsibility; numerical errors propagate to training","Custom objectives disable some optimizations (e.g., GPU acceleration may not work with all custom objectives)","Debugging custom objectives is harder than built-in objectives; requires manual gradient verification"],"requires":["Python 3.7+","NumPy for gradient/Hessian computation","Understanding of calculus and gradient-based optimization","XGBoost 1.0+ (custom objectives available in all recent versions)"],"input_types":["Predictions (NumPy array)","Labels (NumPy array)","Sample weights (optional NumPy array)"],"output_types":["Gradients (NumPy array, same shape as predictions)","Hessians (NumPy array, same shape as predictions)","Metric scores (float)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-xgboost__cap_4","uri":"capability://automation.workflow.early.stopping.with.validation.monitoring","name":"early-stopping-with-validation-monitoring","description":"Monitors evaluation metrics on a held-out validation set during training and stops boosting when validation performance plateaus or degrades, preventing overfitting. XGBoost evaluates the model on validation data after each boosting round, tracks the best metric value, and halts training if no improvement occurs within a configurable patience window (e.g., 10 rounds). Early stopping integrates with custom metrics and supports both single and multi-metric monitoring.","intents":["Prevent overfitting by automatically stopping training when validation performance stops improving","Reduce training time by avoiding unnecessary boosting rounds","Tune the effective number of boosting rounds without manual experimentation"],"best_for":["Data scientists building production models with limited computational budgets","Teams automating hyperparameter tuning and model selection","Practitioners working with imbalanced or noisy datasets prone to overfitting"],"limitations":["Requires a separate validation set; reduces training data available for model fitting","Patience parameter (stopping rounds) is a hyperparameter itself; suboptimal values can stop too early or too late","Early stopping is stochastic if validation set is small; results may vary across runs","Doesn't guarantee global optimum; may stop at local plateau"],"requires":["Python 3.7+","Validation dataset (separate from training data)","XGBoost 0.90+ (early stopping available in all recent versions)"],"input_types":["Validation DMatrix or NumPy array","Validation labels"],"output_types":["Best model state (at iteration with best validation metric)","Training history (metric values per round)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-xgboost__cap_5","uri":"capability://automation.workflow.distributed.training.across.multiple.machines","name":"distributed-training-across-multiple-machines","description":"Distributes training across multiple machines using Rabit (XGBoost's custom distributed communication framework) or external schedulers (Spark, Dask, Kubernetes). XGBoost partitions data across nodes, performs local tree construction in parallel, and synchronizes tree updates via allreduce operations, enabling near-linear scaling on large clusters. Supports both data parallelism (different samples on each node) and feature parallelism (different features on each node) with automatic load balancing.","intents":["Train models on datasets too large for single-machine memory (100GB+)","Reduce training time by distributing computation across a cluster","Integrate XGBoost into existing distributed computing infrastructure (Spark, Dask)"],"best_for":["Data engineering teams with Spark or Dask clusters","Organizations training models on multi-terabyte datasets","Teams with Kubernetes infrastructure seeking distributed ML training"],"limitations":["Distributed training adds communication overhead; beneficial only for datasets >10GB or complex models","Requires network bandwidth between nodes; slow networks (e.g., WAN) negate speedup","Fault tolerance is limited; node failures require restarting from last checkpoint","Debugging distributed training is harder than single-machine; requires distributed logging and monitoring"],"requires":["Python 3.7+","Spark 2.4+ (for PySpark integration) OR Dask 2021.3+ (for Dask integration)","Network connectivity between all nodes","XGBoost 1.0+ with distributed training support"],"input_types":["Spark DataFrame","Dask DataFrame","Distributed DMatrix"],"output_types":["Trained Booster model (collected to single machine)","Training history (aggregated across nodes)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-xgboost__cap_6","uri":"capability://data.processing.analysis.cross.validation.with.stratification","name":"cross-validation-with-stratification","description":"Performs k-fold cross-validation with automatic stratification for classification tasks, evaluating model performance across multiple train/test splits. XGBoost's cv() function partitions data into k folds, trains k models in parallel (one per fold), evaluates each on its held-out fold, and aggregates results (mean and standard deviation of metrics). Supports both stratified (preserves class distribution) and random splitting with custom fold generators.","intents":["Estimate model generalization performance without a separate test set","Tune hyperparameters by evaluating cross-validation scores","Detect overfitting by comparing train and validation metrics across folds"],"best_for":["Data scientists with limited data (small datasets where every sample matters)","Teams performing hyperparameter tuning and model selection","Practitioners validating model stability across different data splits"],"limitations":["Computationally expensive; requires training k models instead of one (k=5 or 10 typical)","Stratification only works for classification; regression requires manual fold specification","Cross-validation estimates variance but not bias; doesn't replace holdout test set for final evaluation","Parallel training across folds requires multi-core CPU or distributed setup"],"requires":["Python 3.7+","Training data (no separate validation set needed)","XGBoost 0.90+ (cv() available in all recent versions)"],"input_types":["DMatrix or NumPy array","Labels (NumPy array)","Custom fold generator (optional)"],"output_types":["Cross-validation results (DataFrame with metric per fold)","Mean and standard deviation of metrics","Trained models (one per fold, optional)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-xgboost__cap_7","uri":"capability://automation.workflow.model.serialization.and.deserialization","name":"model-serialization-and-deserialization","description":"Saves trained models to disk in multiple formats (native XGBoost binary, JSON, text) and loads them for inference or continued training. XGBoost's save_model() and load_model() functions serialize the entire tree ensemble including hyperparameters, feature names, and metadata, enabling model versioning and deployment across environments. Supports both Python pickle (for full Python objects) and language-agnostic formats (JSON, binary) for cross-platform compatibility.","intents":["Save trained models for production deployment and version control","Load pre-trained models for inference without retraining","Share models across different programming languages (Python, R, Java, C++)"],"best_for":["ML engineers deploying models to production systems","Data scientists sharing models across teams or languages","Teams implementing model versioning and experiment tracking"],"limitations":["Native XGBoost format is not human-readable; JSON format is verbose and slower to load","Pickle format is Python-specific and has security risks (arbitrary code execution); avoid for untrusted sources","Model size scales with number of trees and tree depth; large models (>1GB) slow down loading","No built-in model compression; requires external tools for size reduction"],"requires":["Python 3.7+","Trained XGBoost Booster model","Disk space for model file"],"input_types":["Trained Booster object"],"output_types":["Binary file (.model or .bin)","JSON file (.json)","Text file (.txt)","Python pickle file (.pkl)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-xgboost__cap_8","uri":"capability://planning.reasoning.hyperparameter.tuning.integration","name":"hyperparameter-tuning-integration","description":"Integrates with hyperparameter optimization frameworks (Optuna, Ray Tune, Hyperopt) via standard Python APIs, enabling automated search over learning rate, tree depth, regularization, and other parameters. XGBoost's cv() and train() functions return metrics that optimization frameworks use as objectives, supporting both grid search and Bayesian optimization without custom integration code. Supports early stopping within optimization loops to avoid wasting compute on unpromising hyperparameter combinations.","intents":["Automatically search for optimal hyperparameters without manual experimentation","Integrate XGBoost into existing hyperparameter tuning pipelines","Reduce training time by pruning unpromising hyperparameter combinations early"],"best_for":["Data scientists optimizing model performance for competitions or production","Teams with limited domain knowledge about XGBoost hyperparameter sensitivity","Practitioners automating ML pipeline tuning"],"limitations":["Hyperparameter search space is large (10+ parameters); full grid search is infeasible","Bayesian optimization requires many trials (50-200) to converge; expensive for large datasets","Hyperparameter importance varies by dataset; optimal values don't transfer across domains","Tuning is computationally expensive; requires significant compute resources or time"],"requires":["Python 3.7+","Optuna, Ray Tune, or Hyperopt (optional but recommended)","Training and validation data","XGBoost 0.90+"],"input_types":["Hyperparameter search space (dict or Optuna Trial)","Training data (DMatrix or NumPy array)"],"output_types":["Best hyperparameters (dict)","Best metric value (float)","Optimization history (DataFrame)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-xgboost__cap_9","uri":"capability://data.processing.analysis.multi.class.and.multi.output.prediction","name":"multi-class-and-multi-output-prediction","description":"Supports multi-class classification (>2 classes) and multi-output regression (predicting multiple targets simultaneously) via softmax and multi-task learning objectives. XGBoost trains separate tree ensembles for each class/output, sharing the same feature space but learning independent split decisions per class. Predictions return probability distributions (for classification) or multiple regression outputs, enabling complex prediction tasks beyond binary classification.","intents":["Build multi-class classifiers for problems with >2 classes (e.g., image classification, text categorization)","Predict multiple related targets simultaneously (e.g., predicting both price and demand)","Handle imbalanced multi-class datasets with class weights"],"best_for":["Data scientists building multi-class classification systems","Teams predicting multiple correlated outputs (multi-task learning)","Practitioners handling imbalanced datasets with many classes"],"limitations":["Training time scales linearly with number of classes; 100-class problems are slow","Memory usage scales with number of classes; multi-class models are larger than binary","Multi-output regression doesn't share information between outputs; true multi-task learning requires custom objectives","Class imbalance handling is limited; requires manual class weights or resampling"],"requires":["Python 3.7+","Multi-class labels (integers 0 to num_classes-1)","XGBoost 0.90+"],"input_types":["Features (DMatrix or NumPy array)","Labels (NumPy array with class indices or multiple columns for multi-output)"],"output_types":["Probability arrays (num_samples x num_classes for classification)","Predictions (num_samples x num_outputs for regression)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","NumPy and SciPy for numerical operations","Pandas for DataFrame input (optional but recommended)","C++ compiler for building from source (pre-built wheels available)","NVIDIA CUDA 10.0+ (for GPU acceleration) OR ROCm 3.5+ (for AMD GPUs)","Trained XGBoost Booster model","NumPy or Pandas for input data","Sample weights (NumPy array, same length as training data)","XGBoost 0.90+","Matplotlib (for plot_tree) or Graphviz (for external visualization)"],"failure_modes":["Requires manual feature engineering — no automatic feature discovery like neural networks","Memory usage scales with dataset size; approximate splitting trades accuracy for speed on very large datasets","Tree depth and ensemble size must be tuned manually; no automatic architecture search","Single-machine training becomes bottleneck for datasets >100GB; distributed training requires additional setup","GPU acceleration requires NVIDIA CUDA 10.0+ or AMD ROCm; CPU fallback available but slower","GPU memory limits batch size; very large datasets still require chunking","Prediction latency includes GPU transfer overhead (~1-5ms); beneficial only for batch sizes >100","GPU support only available in XGBoost 1.5+; older versions CPU-only","Sample weights are heuristics; no principled way to set optimal weights","Class weights don't address root cause of imbalance; resampling or synthetic data generation may be better","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:20:16.568Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-xgboost","compare_url":"https://unfragile.ai/compare?artifact=pypi-xgboost"}},"signature":"1m9LPrbKEjjHXA4zXnjTwL27ZNLwKcfU+sauZsRvS57KSxG4u8KhAaZvVErZ8Lo+5p52xo8Kn4s6TigqVxBYDQ==","signedAt":"2026-06-15T18:20:54.988Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-xgboost","artifact":"https://unfragile.ai/pypi-xgboost","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-xgboost","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}