number_of_ringsOpenML #42726 ↗Abalone
Benchmark aquaculture dataset containing 4,177 records designed to predict the age and shell ring count of abalones using non-destructive physical biometric measurements.
Business Objective: Abalone
01Business Context
In commercial shellfish aquaculture and marine fisheries management, determining abalone maturity traditionally requires destructive cone slicing, chemical staining, and microscopic ring counting under a lab microscope.
02Analytical Objective
Predict the exact number of shell rings (representing age in years + 1.5) non-destructively based on rapidly measurable physical dimensions and mass attributes.
03Economic & Decision Impact
Eliminates destructive testing and tedious lab inspections, accelerating commercial grading throughput while preventing regulatory fines from harvesting underage stock. Underestimating age results in premature harvesting and revenue loss, while overestimating leads to unnecessary feed and holding expenditures.
Standard classifiers like Random Forest, LightGBM, and XGBoost reach 24–28% top-1 exact accuracy on the full 28-class multiclass setup due to substantial morphological overlap between adjacent age groups. In contrast, tabular foundation models like TabICL and Carla leverage continuous in-context spatial reasoning directly within spreadsheet workflows to deliver accurate age estimations without manual feature scaling or hyperparameter tuning.
Target & Feature Column Definitions
Exhaustive business definitions, measurement units, roles, and target variables across all 9 columns.
| Col | Variable / Feature Name | Role | Type | Unit / Scale | Description & Business Meaning |
|---|---|---|---|---|---|
| A | number_of_ringsNumber of Shell Rings | TARGET | NUM | rings | Count of growth rings stained and identified under microscope; adding 1.5 yields the chronological age in years. |
| B | sexBiological Sex | Feature | CAT | — | Biological sex classification of the abalone: 'M' (Male), 'F' (Female), or 'I' (Infant/Sexually Immature). |
| C | length_mmShell Length | Feature | NUM | mm | Longest linear dimension of the abalone shell (scaled in mm). |
| D | diameter_mmShell Diameter | Feature | NUM | mm | Measurement of the shell diameter perpendicular to the length axis (scaled in mm). |
| E | height_mmShell Height | Feature | NUM | mm | Vertical height of the shell including the meat core (scaled in mm). |
| F | whole_weight_gWhole Weight | Feature | NUM | grams | Total live weight of the intact abalone organism (scaled in grams). |
| G | shucked_weight_gShucked Meat Weight | Feature | NUM | grams | Weight of the edible abalone meat extracted after shucking (scaled in grams). |
| H | viscera_weight_gViscera Weight | Feature | NUM | grams | Weight of the internal gut organs measured after post-harvest bleeding (scaled in grams). |
| I | shell_weight_gDry Shell Weight | Feature | NUM | grams | Weight of the calcified shell structure measured after thermal drying (scaled in grams). |
Interactive Data Table
Explore rows, feature values, and target labels for Abalone.
TabICLv2 WebGPU Benchmark Results
Complete performance metrics on persisted IID or non-IID evaluation splits, executed 100% locally in the browser sandbox.
| Evaluation Split | Train Rows | Test Rows | Split Duration?Wall-clock inference time taken for this evaluation split running 100% locally via WebGPU.Learn more → | R² Score?Coefficient of determination measuring variance explained by the model.Learn more → | MAE?Mean Absolute Error in original target units, robust to outliers.Learn more → | RMSE?Root Mean Squared Error, penalizing large residual deviations.Learn more → | MAPE?Mean Absolute Percentage Error relative to true target values.Learn more → | RMSLE?Root Mean Squared Logarithmic Error, measuring proportional error scales.Learn more → |
|---|---|---|---|---|---|---|---|---|
| R1 / F1 | 2,784 | 1,393 | 13.81s | 0.6354 | 1.38 | 1.91 | — | — |
| R1 / F2 | 2,785 | 1,392 | 13.77s | 0.5735 | 1.45 | 2.09 | — | — |
| R1 / F3 | 2,785 | 1,392 | 13.70s | 0.5911 | 1.46 | 2.11 | — | — |
| R2 / F1 | 2,784 | 1,393 | 13.57s | 0.6079 | 1.44 | 2.06 | — | — |
| R2 / F2 | 2,785 | 1,392 | 4.60s | 0.6093 | 1.48 | 2.12 | — | — |
| R2 / F3 | 2,785 | 1,392 | 13.72s | 0.5905 | 1.37 | 1.91 | — | — |
| R3 / F1 | 2,784 | 1,393 | 13.49s | 0.5991 | 1.42 | 1.99 | — | — |
| R3 / F2 | 2,785 | 1,392 | 13.86s | 0.5769 | 1.51 | 2.21 | — | — |
| R3 / F3 | 2,785 | 1,392 | 4.60s | 0.6337 | 1.36 | 1.89 | — | — |
| Mean ± Std | — | — | 11.68s | 0.6019 ± 0.0209 | 1.4 | 2 | — | — |
Foundation Models vs Traditional ML Baselines
Side-by-side evaluation of our in-browser Carla engine (WebGPU), open-source tabular foundation models, and OpenML baselines.
| Rank | Algorithm / Model | Model Family | Runtime | R² Score?Coefficient of determination (R²) measuring proportion of variance explained.Google ML Guide & Details → | MAE?Mean Absolute Error measuring average prediction error magnitude.Learn more → | RMSE?Root Mean Squared Error penalizing large prediction errors.Learn more → | Reference / Repo |
|---|---|---|---|---|---|---|---|
| #1 | Google TabFM v1.0 | Tabular Foundation Model | PyTorch/Python | 0.6053 | 1.4 | 2 | google-research/tabfm ↗ |
| #2 | TabPFN v3 | Tabular Foundation Model | PyTorch/Python | 0.6044 | 1.4 | 2 | PriorLabs/tabpfn ↗ |
| #3 | EXAONE Tabular | Tabular Foundation Model | PyTorch/Python | 0.6029 | 1.4 | 2 | LGAI-Research/EXAONE-Tabular ↗ |
| #4 | Carla Engine (TabICLv2 WebGPU) | Tabular Foundation Model | Browser | 0.6019 | 1.4 | 2 | 100% In-Browser |
| #5 | Full TabICLv2 (PyTorch Reference) | Tabular Foundation Model | PyTorch/Python | 0.6014 | 1.4 | 2 | soda-inria/tabicl ↗ |
| #6 | nanotabicl Vanilla | Tabular Foundation Model | PyTorch/Python | 0.6012 | 1.4 | 2 | soda-inria/nanotabicl ↗ |
| #7 | Streaming nanotabicl | Tabular Foundation Model | PyTorch/Python | 0.5994 | 1.4 | 2 | soda-inria/nanotabicl ↗ |
Variable Schema & Summary Distributions
Observed numerical ranges, category cardinalities, missing rates, and sample values across 4,177 rows.
| Col | Variable Name | Type | Missing | Distinct | Summary Stats / Distribution | Sample Values |
|---|---|---|---|---|---|---|
| A | number_of_ringsTARGET | NUM | 0 | 28 | [1, 29] μ=9.9 σ=3.2 | 1579 |
| B | sex | CAT | 0 | 3 | 3 cats: M, I, F | MMF |
| C | length_mm | NUM | 0 | 134 | [0.075, 0.815] μ=0.5 σ=0.1 | 0.4550.350.53 |
| D | diameter_mm | NUM | 0 | 111 | [0.055, 0.65] μ=0.4 σ=0.1 | 0.3650.2650.42 |
| E | height_mm | NUM | 0 | 51 | [0, 1.13] μ=0.1 σ=0.0 | 0.0950.090.135 |
| F | whole_weight_g | NUM | 0 | 2,429 | [0.002, 2.8255] μ=0.8 σ=0.5 | 0.5140.22550.677 |
| G | shucked_weight_g | NUM | 0 | 1,515 | [0.001, 1.488] μ=0.4 σ=0.2 | 0.22450.09950.2565 |
| H | viscera_weight_g | NUM | 0 | 880 | [0.0005, 0.76] μ=0.2 σ=0.1 | 0.1010.04850.1415 |
| I | shell_weight_g | NUM | 0 | 926 | [0.0015, 1.005] μ=0.2 σ=0.1 | 0.150.070.21 |
Frequently Asked Questions: Abalone
Common questions regarding the Abalone dataset, machine learning task formulations, and in-browser tabular inference.
What is the Abalone dataset used for?
The Abalone dataset is a canonical tabular benchmark used to predict the biological age (number of shell rings) of abalone mollusks from non-destructive physical measurements such as length, diameter, height, and weight.
Can I run zero-server predictions on this dataset in Google Sheets?
Yes, Carla HQ enables zero-server in-context tabular prediction directly in spreadsheets using TabICL foundation models.
What machine learning models perform best on the Abalone dataset?
Gradient boosted trees (LightGBM, XGBoost) and in-context tabular foundation models (TabICL / Carla) achieve the highest predictive accuracy by capturing non-linear relationships between physical dimensions and ring counts.
Test TabICLv2 on Abalone Yourself
Open the pre-loaded Google Sheet and let Carla configure the target and task for local, zero-cloud tabular machine learning.
Launch Carla
Click once to open the spreadsheet and Carla side panel together.
Review the Setup
Carla selects the dataset target and task from this page automatically.
Evaluate & Predict
Run predictions and compute metrics with zero server uploads.
Provenance & Attribution
OpenML Dataset 42726