What are some common mistakes in training and evaluating AI models?

What are some common mistakes in training and evaluating AI models?

Some common mistakes include overfitting, not using a diverse dataset, and neglecting to validate the model properly.

How can I avoid overfitting in my model?

You can avoid overfitting by using techniques like cross-validation, regularization, and ensuring you have enough training data.

What is cross-validation?

Cross-validation is a technique used to assess how the results of a statistical analysis will generalize to an independent dataset.

Why is it important to use a diverse dataset?

Using a diverse dataset helps ensure that the model can generalize well to different scenarios and reduces bias.

What are some validation techniques I should consider?

You should consider techniques like k-fold cross-validation, holdout validation, and stratified sampling to evaluate your model.