decision-tree-analysis

name	decision-tree-analysis
description	Use when building a decision tree model in R and generating feature importance ranking outputs. Supports classification and regression, automatic task detection, parameter validation, model evaluation summaries, and exports of feature-importance tables and figures.
license	MIT
author	AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Decision Tree Analysis

Use this skill to train a decision tree model from a tabular file and export feature importance ranking results.

Use This Skill When

• You need a decision tree workflow in R for either classification or regression.
• You need feature importance ranking as a table and a figure.
• You need a command-line workflow with parameter validation and standardized output folders.

Primary Command

Rscript scripts/main.R \
  --data_file <input_file> \
  --target_var <target_column> \
  --task_type <auto|classification|regression> \
  --output_dir <output_dir>

Prerequisites

• Rscript is available in the shell.
• Required R packages: optparse, data.table, rpart.
• Install missing packages with Rscript -e 'install.packages(c("optparse", "data.table", "rpart"), repos="https://cloud.r-project.org")'.

Core Arguments

Argument	Required	Description
--data_file	Yes	Input data file in CSV, TXT, or TSV format
--target_var	Yes	Target column to predict
--task_type	No	auto, classification, or regression. Default auto
--output_dir	No	Output directory, default ./Decision_Tree_Results
--train_ratio	No	Train set ratio between 0 and 1, default 0.7
--max_depth	No	Maximum tree depth, default 5
--minsplit	No	Minimum observations required to attempt a split, default 10
--minbucket	No	Minimum observations allowed in a terminal node, default 3
--cp	No	Complexity parameter for pruning, default 0.001
--seed	No	Random seed, default 42
--exclude_vars	No	Comma-separated columns to exclude from modeling
--importance_top_n	No	Number of top features to show in the importance plot, default 15
--output_format	No	Table output format: csv or txt, default csv

Input Requirements

• The input file must contain the target column.
• All predictor columns come from the remaining columns after excluding target_var and exclude_vars.
• If the first column is unnamed or uses an ID-like name such as id or rowname, and its values are unique, the skill automatically treats it as row names instead of a predictor.
• Rows with missing values in any modeling column are removed before training.
• Character predictors are automatically converted to factors.
• In auto mode, a numeric target with more than 10 unique values is treated as regression; otherwise it is treated as classification.
• At least 5 complete rows are required after filtering.

Example input:

study_hours,sleep_hours,attendance,score_band
3.5,7.0,0.88,medium
5.0,6.5,0.95,high
2.0,8.0,0.75,low

Minimal Workflow

1. Confirm the input file exists and the target column name is correct.
2. Run scripts/main.R with the target column and optional modeling parameters.
3. Check the output directory for feature importance tables under table/ and the ranking plot under figure/.

If you omit --data_file or --target_var, the script exits with SKILL_MISSING_INPUT.

Outputs

Expected output structure:

<output_dir>/
├── data/
├── table/
└── figure/

Primary result files:

• table/decision_tree_feature_importance.<csv|txt>
• table/decision_tree_metrics.csv
• figure/decision_tree_feature_importance.pdf

Additional files:

• data/decision_tree_predictions.csv
• data/decision_tree_model.rds

Notes:

• Exactly one feature-importance table is written per run. The file extension is controlled by --output_format.
• Evaluation metrics are saved to table/decision_tree_metrics.csv.
• If the fitted tree does not split, the run completes but emits a warning because feature importances and predictions may be degenerate on very small training sets.

Feature importance result fields include:

• rank
• feature
• importance
• relative_importance

Choose the Task Type

• Use classification for categorical targets such as yes/no, risk_level, or species.
• Use regression for continuous numeric targets such as price, score, or yield.
• Use auto when the target type is obvious and you want the script to infer it.

Read These Files When Needed

Need	File
Decision tree method and feature importance details	references/algorithm.md
More CLI examples	references/cli-guide.md
Error diagnosis	references/troubleshooting.md
Main execution entry point	scripts/main.R
Sample test data	tests/data/

Test Data

• tests/data/dt_sample1.csv: CSV classification sample with an unnamed first column automatically recognized as row names. Suggested target: fustat.
• tests/data/dt_sample2.csv: CSV classification sample with an unnamed first column automatically recognized as row names. Suggested target: fustat.
• tests/data/dt_sample3.txt: Tab-delimited high-dimensional classification sample with an unnamed first column automatically recognized as row names. Suggested target: Group.

Quick Examples

Classification:

Rscript scripts/main.R \
  --data_file tests/data/dt_sample1.csv \
  --target_var fustat \
  --task_type classification \
  --max_depth 4 \
  --output_dir tests/output_dt_sample1_classification

Classification on a second CSV sample:

Rscript scripts/main.R \
  --data_file tests/data/dt_sample2.csv \
  --target_var fustat \
  --task_type classification \
  --output_dir tests/output_dt_sample2_classification

TXT input example:

Rscript scripts/main.R \
  --data_file tests/data/dt_sample3.txt \
  --target_var Group \
  --task_type classification \
  --max_depth 4 \
  --output_dir tests/output_dt_sample3_classification

Validation

Rscript scripts/main.R --help

Rscript scripts/main.R \
  --data_file tests/data/dt_sample1.csv \
  --target_var fustat \
  --task_type classification \
  --output_dir tests/validation_dt_sample1

Rscript scripts/main.R \
  --data_file tests/data/dt_sample2.csv \
  --target_var fustat \
  --task_type classification \
  --output_dir tests/validation_dt_sample2

Rscript scripts/main.R \
  --data_file tests/data/dt_sample3.txt \
  --target_var Group \
  --task_type classification \
  --output_dir tests/validation_dt_sample3

After running analysis, verify that the following exist:

• tests/validation_dt_sample1/table/decision_tree_feature_importance.csv
• tests/validation_dt_sample1/table/decision_tree_metrics.csv
• tests/validation_dt_sample1/figure/decision_tree_feature_importance.pdf
• tests/validation_dt_sample2/table/decision_tree_feature_importance.csv
• tests/validation_dt_sample2/table/decision_tree_metrics.csv
• tests/validation_dt_sample2/figure/decision_tree_feature_importance.pdf
• tests/validation_dt_sample3/table/decision_tree_feature_importance.csv
• tests/validation_dt_sample3/table/decision_tree_metrics.csv
• tests/validation_dt_sample3/figure/decision_tree_feature_importance.pdf

Common Errors

• SKILL_FILE_NOT_FOUND: Input file path is wrong or inaccessible.
• SKILL_MISSING_COLUMNS: The target column or requested excluded columns are missing.
• SKILL_INVALID_DATA: Input data is malformed or unsuitable for model training.
• SKILL_INVALID_PARAMETER: An argument value is invalid.
• SKILL_INSUFFICIENT_DATA: Too few usable rows or classes remain after filtering.
• SKILL_DEPENDENCY_MISSING: A required R package such as optparse, data.table, or rpart is unavailable.

If a run succeeds but logs that the decision tree did not split, lower --minsplit and --minbucket or provide more training rows before trusting the ranking output.

If the issue is not obvious, read references/troubleshooting.md.