Decision Tree Analysis

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

bashCopy code
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

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:

csvCopy code
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:

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<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

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:

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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:

bashCopy code
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:

bashCopy code
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

bashCopy code
Rscript scripts/main.R --help

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

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

bashCopy code
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.