r/DevDepth

Overfitting is one of the most common problems beginners hit when training machine learning models. Your training accuracy looks great but validation accuracy tanks. Here's how to fix it.

**What's actually happening:**

Your model is memorising the training data instead of learning patterns. It works perfectly on data it's seen, and fails on anything new.

**Practical fixes in order of ease:**

1. **Get more data** — The most reliable fix. Overfitting shrinks when your dataset grows.

2. **Simplify your model** — Fewer layers, fewer neurons, fewer features. Start simple and add complexity only when needed.

3. **Regularisation** — Add L2 (Ridge) or L1 (Lasso) penalties to your loss function. In Keras: `kernel_regularizer=l2(0.001)`

4. **Dropout** — Randomly deactivate neurons during training. Add `Dropout(0.3)` after dense layers.

5. **Early stopping** — Stop training when validation loss stops improving:

`EarlyStopping(patience=5, restore_best_weights=True)`

6. **Cross-validation** — Use k-fold CV instead of a single train/test split to get a honest picture of performance.

**Quick diagnostic:** Plot your training vs validation loss over epochs. If training loss keeps falling while validation loss rises, you're overfitting.

Which of these has worked best for you?