# MNIST Classification — Binary, Multiclass, Multilabel & Multioutput

A complete tour of classification approaches on the MNIST handwritten digit dataset (70,000 images, 28×28 pixels).

## Overview

Covers four distinct classification problem types on the same dataset, comparing models honestly with cross-validation rather than relying on raw accuracy (which is misleading on imbalanced classes).

## Approach

1. **Binary classification** ("is it an 8?") — SGD Classifier baseline, evaluated against a `DummyClassifier` to prove the model actually learned something beyond guessing the majority class.
2. **Threshold analysis** — precision-recall and ROC curves to show the tradeoff between precision and recall as the decision threshold moves, instead of reporting one fixed operating point.
3. **Model comparison** — SGD (linear) vs Random Forest (ensemble) on the same binary task.
4. **Multiclass classification** — all 10 digits at once, with a 10×10 confusion matrix (normalized by row) to see which digits actually get confused with which.
5. **Multilabel classification** — predicting multiple independent yes/no labels per sample simultaneously, including `ClassifierChain` to model dependence between labels.
6. **Multioutput classification** — predicting all 784 pixel values at once (image denoising as a classification problem).

## Results

| Model / Task | Metric | Score |
|---|---|---|
| SGD (binary: "is it an 8?") | F1 | 0.58 |
| SGD (binary) | ROC AUC | 0.915 |
| Random Forest (binary) | F1 | 0.90 |
| Random Forest (binary) | ROC AUC | 0.997 |
| Multilabel (KNN) | Macro F1 | 0.978 |

The SGD → Random Forest jump illustrates why a linear decision boundary struggles on raw pixel data compared to a tree ensemble that can carve non-linear regions.

## Tech stack

Python, scikit-learn, matplotlib

## Notebooks

- [`classification.ipynb`](classification.ipynb) — original notebook, my own work
- [`classification_notebook_polished.ipynb`](classification_notebook_polished.ipynb) — same analysis with AI-assisted documentation and formatting added for readability