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xgboost-analysis

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Use when building XGBoost models on tabular data and returning feature importance ranking outputs. Supports binary classification and regression with automatic task detection, train-test split, performance tables, feature importance ranking tables, and PNG importance plots.

When to use

• You need a command-line XGBoost workflow in R for tabular data.
• You need a reproducible train-test split, model training, and evaluation.
• You need feature importance ranking outputs as both a table and a figure.
• You need automatic one-hot encoding for categorical predictors.
• Your data may contain a first unnamed sample ID column such as `V1` that should not enter the model.

When not to use

• Your classification target has more than 2 classes.
• Your input is not tabular CSV, TXT, or TSV data.
• You need causal interpretation, mechanism claims, or policy, business, or clinical conclusions.
• You only need narrative interpretation or triage of an existing result rather than model training.

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