How does tuning affect the performance of AI models?

How does tuning affect the performance of AI models?

Tuning can significantly enhance model performance by optimizing hyperparameters, leading to better accuracy and efficiency.

What are some common hyperparameters that need tuning?

Common hyperparameters include learning rate, batch size, number of layers, and dropout rate.

Can you give an example of how tuning improved a model?

Sure! For instance, tuning the learning rate in a neural network can reduce training time and improve accuracy by finding the optimal convergence point.

Is there a risk of overfitting when tuning?

Yes, excessive tuning can lead to overfitting, where the model performs well on training data but poorly on unseen data.

How can I avoid overfitting during tuning?

You can use techniques like cross-validation, regularization, and early stopping to mitigate overfitting during the tuning process.