Multimodal Tabular Automl Eval

---
name: multimodal-tabular-automl-eval
description: Evaluates automated machine learning strategies for supervised learning on multimodal tabular datasets containing text, numeric, and categorical features. It probes how well different featurization methods, neural backbones, and ensemble aggregation techniques handle mixed data types and extract predictive signal from text fields. Use when the user wants to benchmark on Multimodal Tabular Benchmark (18 datasets), or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2111.02705
  bibtex_key: [REDACTED]
  confidence: high
---

# multimodal-tabular-automl-eval

> Benchmarking Multimodal AutoML for Tabular Data with Text Fields — Shi et al. (2021) (arXiv:2111.02705, 2021)

## What this evaluates

Evaluates automated machine learning strategies for supervised learning on multimodal tabular datasets containing text, numeric, and categorical features. It probes how well different featurization methods, neural backbones, and ensemble aggregation techniques handle mixed data types and extract predictive signal from text fields.

## Datasets

- **Multimodal Tabular Benchmark (18 datasets)** — total ?; splits: train/val/test (-1); repo https://github.com/sxjscience/autom1MULTIModal_benchmark

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Fraction of correctly classified instances out of the total number of instances.
- `AUC` — range: [0, 1]
  - Area under the Receiver Operating Characteristic curve, measuring the ability of a binary classifier to distinguish between classes across all classification thresholds.
- `R^2` — range: other
  - Coefficient of determination for regression tasks, representing the proportion of variance in the dependent variable predictable from the independent variables.

## Input / output format

**Input**: Tabular rows containing mixed modalities: text fields, numeric features, and categorical features.

**Output**: Predicted class labels (for classification) or continuous values (for regression).

## Scoring recipe

```python
def score(predictions, gold, task_type):
    if task_type == 'binary_classification':
        return roc_auc_score(gold, predictions)
    elif task_type == 'multiclass_classification':
        return accuracy_score(gold, predictions)
    elif task_type == 'regression':
        return r2_score(gold, predictions)
```

## Common pitfalls

- Off-the-shelf AutoML tools (e.g., H2O) may automatically treat text columns as categorical, effectively ignoring the text features unless explicitly featurized beforehand.
- The coefficient of determination (R^2) can be negative for poor predictions, despite the paper noting it lies in [0, 1] for reasonable predictions.
- Featurization strategy choice (e.g., N-Grams vs. pretrained Transformer embeddings) drastically alters the predictive signal available to tabular models, making cross-method comparisons highly sensitive to preprocessing decisions.

## Evidence (verbatim from paper)

> We evaluate regression tasks via the coefficient of determination  $R^2$ , multiclass classification tasks via accuracy, and binary classification tasks via area under the ROC curve (AUC).

## Citation

```bibtex
@misc{shi2021benchmarkingmultimodalautoml,
  title={Benchmarking Multimodal AutoML for Tabular Data with Text Fields},
  author={Shi et al. (2021)},
  year={2021},
  note={arXiv:2111.02705}
}
```

- arXiv: 2111.02705