# MNIST Classification A full tour of classification problem types on the MNIST handwritten digit dataset — binary, multiclass, multilabel, and multioutput — with an emphasis on evaluating models honestly rather than trusting raw accuracy. ## Why not just report accuracy? About 90% of MNIST digits are *not* an 8. A classifier that always guesses "not 8" scores ~90% accuracy while learning nothing. This project compares models with ROC AUC and F1 instead, and validates every "is it learning something real" claim against a `DummyClassifier` baseline. ## Linear vs ensemble: SGD vs Random Forest Same binary task ("is it an 8?"), two very different models: ![ROC curve comparison](images/clf-roc-comparison.png) | Model | F1 | ROC AUC | |---|---|---| | SGD (linear) | 0.58 | 0.915 | | Random Forest (ensemble) | 0.90 | 0.997 | The gap illustrates a core limitation of linear models: SGD can only draw one straight decision boundary through 784-dimensional pixel space, while Random Forest's ensemble of trees carves non-linear regions that fit the actual shape of the data. ## Where the multiclass model actually gets confused Extending to all 10 digits, the normalized confusion matrix shows exactly which digits get mistaken for which — more useful than a single accuracy number: ![Multiclass confusion matrix](images/clf-confusion-matrix.png) ## Multioutput: predicting an entire image Pushed the idea further — instead of predicting a label, predicting all 784 pixel values at once, framed as denoising a noisy digit:


Original	Noisy input	Model's denoised output

## Notebooks - [`classification.ipynb`](../classification-project/classification.ipynb) — original work - [`classification_notebook_polished.ipynb`](../classification-project/classification_notebook_polished.ipynb) — AI-assisted documentation pass