Lesson 5 of 8
Hyperparameters that matter most
In plain words: Hyperparameters are the settings you choose before training. A few matter far more than the rest.
- Learning rate: how big each update step is. The single most important setting.
- Epochs: how many passes over the data. Use early stopping instead of guessing.
- Batch size: examples per update. Bigger is faster but needs more GPU memory.
- For tree models: number of trees, depth and learning rate.
- Tune automatically with Optuna or your cloud's tuning service once a baseline works.
Tip Change one thing at a time and log every run in an experiment tracker.