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pokemon-automl-model
VictorTishkevich/pokemon-automl-model
pokemon-automl-model is a machine learning model from VictorTishkevich. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This model classifies Pokémon from the 151 generation dataset as Common (0) or Rare (1). It trains a tabular classifier with AutoGluon Tabular for the Carnegie Mellon University 24-679 course.
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Public
Updated Sep 23, 2026
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At a glance
- Access
- Public
- Created
- Sep 21, 2026
- Updated
- Sep 23, 2026
- SHA
- 01ed3cb7
Model card
From the Hugging Face model README
24-679: Pokémon Rarity Tabular Classifier
This model classifies Pokémon from the 151 generation dataset as Common (0) or Rare (1). It trains a tabular classifier with AutoGluon Tabular for the Carnegie Mellon University 24-679 course.
The intended use is teaching tabular data preprocessing, AutoML classification, and evaluation. It classifies rarity based on numeric attributes (HP, attack count, retreat cost) and categorical features (type, stage, ability).
Inputs and outputs
Supply a pandas DataFrame with features hp, attack_count, retreat_cost, type, stage, and has_ability. predict() returns 0 (Common) or 1 (Rare), and predict_proba() returns class probabilities.
| Label | Meaning |
|---|---|
0 | Common |
1 | Rare |
Data and training
Source: pakiino/2026-24679-pokemon-151-tabular-hw1.
| Partition | Total |
|---|---|
| Training | 336 |
| Validation | 4 |
| Test | 5 |
| Training setting | Recorded value |
|---|---|
| Framework | AutoGluon Tabular 1.6.1 |
| Target | label |
| Problem type | binary |
| Preset | medium_quality |
| Selection metric | Balanced accuracy |
| Fit budget | 300 seconds |
| Best Model | WeightedEnsemble_L2 (NeuralNetTorch) |
| Recorded environment | Linux; Python 3.13.15; PyTorch 2.11.0+cpu |
The course notebook reports 19.66 seconds of total training time. The best model achieved a validation score (balanced_accuracy) of 1.0 on the 4 validation rows.
Evaluation
| Test metric | Value |
|---|---|
| Test size | 5 original responses |
| One error change | 20.0 percentage points |
| Training-majority baseline predicts | Common |
This small test set of 5 examples is not a comprehensive evaluation. Broader use requires a larger evaluation dataset.
- Created
- Sep 21, 2026
- Updated
- Sep 23, 2026