---
id: "airfoil-self-noise"
slug: "airfoil-self-noise"
name: "Airfoil Self Noise"
domain: "UCI"
taskType: "regression"
numRows: 1503
numColumns: 6
targetColumn: "scaled-sound-pressure"
primaryMetric: "RMSE"
primaryScore: "1.12"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-tabular-benchmark-containers/resolve/main/containers/airfoil_self_noise/01a05c1d-ca54-7b04-b327-6ee96e41a319/dataset.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/1nhnWfnL5X-CfaTZANOolDpu1fOnw58VkHrDD8dUwU-g/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/airfoil-self-noise"
---

# Dataset: Airfoil Self Noise (airfoil-self-noise)

> Airfoil Self Noise dataset with 1,503 records for regression.

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `regression` |
| **Business Domain** | UCI |
| **Target Column** | `scaled-sound-pressure` |
| **Rows (Instances)** | 1,503 |
| **Features (Columns)** | 6 |
| **Primary Evaluation Metric** | RMSE |
| **Best Benchmark Score** | **1.12** |

## Business Decision Context
**Context:** Curated tabular benchmark dataset from UCI.
**Objective:** Predict target variable 'scaled-sound-pressure' using available features.
**Decision Impact:** High accuracy models enable automated data-driven decision-making.

## Feature Schema
| Column Name | Data Type | Role | Distinct Values | Missing Values (%) | Description |
| :--- | :--- | :--- | :--- | :--- | :--- |
| `frequency` | `numeric` | feature | 21 | 0 | Feature attribute for Airfoil Self Noise |
| `attack-angle` | `string` | feature | 27 | 0 | Feature attribute for Airfoil Self Noise |
| `chord-length` | `numeric` | feature | 6 | 0 | Feature attribute for Airfoil Self Noise |
| `free-stream-velocity` | `numeric` | feature | 4 | 0 | Feature attribute for Airfoil Self Noise |
| `suction-side-displacement-thickness` | `numeric` | feature | 105 | 0 | Feature attribute for Airfoil Self Noise |
| `scaled-sound-pressure` | `numeric` | target | 1,456 | 0 | Target variable for Airfoil Self Noise |

## Empirical Benchmark Leaderboard
| Rank | Algorithm / Model | Model Family | Runtime | R² Score | MAE | RMSE |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | **TabPFN v3** | Tabular Foundation Model | PyTorch/Python | **0.9778** | 0.7 | 1 |
| 2 | **Google TabFM v1.0** | Tabular Foundation Model | PyTorch/Python | **0.9740** | 0.7 | 1.1 |
| 3 | **Carla Engine (TabICLv2 WebGPU)** | Tabular Foundation Model | Browser | **0.9737** | 0.7 | 1.1 |
| 4 | **nanotabicl Vanilla** | Tabular Foundation Model | PyTorch/Python | **0.9733** | 0.7 | 1.1 |
| 5 | **Full TabICLv2 (PyTorch Reference)** | Tabular Foundation Model | PyTorch/Python | **0.9733** | 0.7 | 1.1 |
| 6 | **Streaming nanotabicl** | Tabular Foundation Model | PyTorch/Python | **0.9733** | 0.7 | 1.1 |
| 7 | **EXAONE Tabular** | Tabular Foundation Model | PyTorch/Python | **0.9715** | 0.7 | 1.2 |

### Repeated Cross-Validation Fold Scores (TabICLv2 Reference)
| Fold | Train Samples | Test Samples | Metric Score | Latency |
| :--- | :--- | :--- | :--- | :--- |
| Fold 0 | 1002 | 501 | 1.2379 | 951ms |
| Fold 1 | 1002 | 501 | 0.9880 | 631ms |
| Fold 2 | 1002 | 501 | 1.0388 | 661ms |
| Fold 0 | 1002 | 501 | 1.1070 | 625ms |
| Fold 1 | 1002 | 501 | 1.2598 | 665ms |
| Fold 2 | 1002 | 501 | 1.0243 | 631ms |
| Fold 0 | 1002 | 501 | 0.9730 | 649ms |
| Fold 1 | 1002 | 501 | 1.2590 | 619ms |
| Fold 2 | 1002 | 501 | 1.1269 | 654ms |
| Fold 0 | 1002 | 501 | 1.2052 | 634ms |
| Fold 1 | 1002 | 501 | 0.9992 | 645ms |
| Fold 2 | 1002 | 501 | 1.2040 | 637ms |
| Fold 0 | 1002 | 501 | 1.0404 | 661ms |
| Fold 1 | 1002 | 501 | 1.2180 | 637ms |
| Fold 2 | 1002 | 501 | 1.2836 | 634ms |
| Fold 0 | 1002 | 501 | 1.1232 | 649ms |
| Fold 1 | 1002 | 501 | 1.2848 | 647ms |
| Fold 2 | 1002 | 501 | 1.1261 | 620ms |
| Fold 0 | 1002 | 501 | 1.0210 | 651ms |
| Fold 1 | 1002 | 501 | 1.1553 | 644ms |
| Fold 2 | 1002 | 501 | 1.1251 | 652ms |
| Fold 0 | 1002 | 501 | 1.1831 | 632ms |
| Fold 1 | 1002 | 501 | 1.1947 | 648ms |
| Fold 2 | 1002 | 501 | 1.1174 | 629ms |
| Fold 0 | 1002 | 501 | 1.1083 | 641ms |
| Fold 1 | 1002 | 501 | 0.9916 | 640ms |
| Fold 2 | 1002 | 501 | 1.1004 | 647ms |
| Fold 0 | 1002 | 501 | 1.0128 | 624ms |
| Fold 1 | 1002 | 501 | 1.0515 | 641ms |
| Fold 2 | 1002 | 501 | 1.1030 | 648ms |

## Provenance & Downloadable Artifacts
- **Dataset Parquet File:** [Direct Parquet Download](https://huggingface.co/datasets/carlahq/demo-tabular-benchmark-containers/resolve/main/containers/airfoil_self_noise/01a05c1d-ca54-7b04-b327-6ee96e41a319/dataset.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/1nhnWfnL5X-CfaTZANOolDpu1fOnw58VkHrDD8dUwU-g/edit?usp=sharing)
- **Academic Paper / Citation:** [Read Research Paper](https://doi.org/10.24432/C5VW2C)
- **Canonical HTML Page:** [https://carlahq.eu/datasets/airfoil-self-noise](https://carlahq.eu/datasets/airfoil-self-noise)