Upload labeled data, get gradient boosting predictions with SHAP importance. Free.
or click to browse · max 3 MB
Running xgboost classification with shap explainability analysis...
Sent to — interactive charts, statistical results, R code, and AI insights.
Analyze another fileGradient boosting classifier that builds an ensemble of decision trees. Provides SHAP feature importance, ROC curves, and confusion matrices for binary classification tasks.
Use this when you need to predict a binary outcome (yes/no, churn/stay) and want high accuracy with feature importance.
If you need interpretable coefficients (not just importance), use Logistic Regression.
Built for: Data scientist, ML engineer, analyst
Typical data source: Labeled dataset with a binary target and numeric/categorical features
Classification dataset
Minimum 100 rows · Best with 500-50000 rows
XGBoost gradient boosting classifier with SHAP feature importance, learning curves, ROC curve, and confusion matrix. Predicts high-value transactions from retail data features.
AUC, accuracy, precision, recall, and F1 score
Gain-based feature importance ranking
Mean absolute SHAP values per feature
Receiver operating characteristic curve with AUC
Predicted vs actual class labels
Train vs test AUC across boosting rounds — overfitting detection
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Logistic — Need interpretable coefficients
Need more power? Random Forest — Want bagging instead of boosting
Similar: Naive Bayes
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
Run any analysis on your own data — 60+ validated R modules, interactive reports, AI insights, and PDF export.
Try Free — No Credit Card