CatBoost Hyperparameter Tuning Results for a Single Fold

5.5 KB of performance metrics from a CatBoost model optimization experiment. The data, authored by Prosenjit Das, captures results from varying tree depth for a single cross-validation fold. It was last updated on June 1, 2026, and is shared under a CC-BY-4.0 license.

Use Cases

• Analyzing the impact of tree depth on model performance based on the described tuning experiment.
• Benchmarking CatBoost hyperparameter results against other optimization runs.
• Visualizing the relationship between a single hyperparameter and model metrics for a specific data fold.

Strengths

• File size is 5.5 KB, making it trivial to download and inspect.
• Released under a permissive CC-BY-4.0 license for easy reuse.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Prosenjit Das via figshare

Collection Method

Likely generated from a machine learning hyperparameter optimization experiment.

Freshness

Last updated 2026-06-01 17:51:54; freshness should be verified.