What are some common mistakes in segmentation models?

What are some common mistakes in segmentation models?

Some common mistakes include using inappropriate metrics, not preprocessing data properly, and overfitting.

Can you explain why overfitting is a problem?

Overfitting occurs when a model learns noise in the training data, leading to poor performance on unseen data.

How can I avoid overfitting in my segmentation model?

You can avoid overfitting by using techniques like cross-validation, regularization, and simplifying your model.

What preprocessing steps should I consider?

Important preprocessing steps include normalization, handling missing values, and feature selection.