What are some common mistakes in classification tasks?

What are some common mistakes in classification tasks?

Some common mistakes include using imbalanced datasets, not preprocessing data properly, and overfitting the model.

Can you explain how imbalanced datasets affect classification?

Imbalanced datasets can lead to biased models that favor the majority class, resulting in poor performance on the minority class.

What preprocessing steps should I take to avoid these mistakes?

You should normalize your data, handle missing values, and consider techniques like oversampling or undersampling for imbalanced data.

How can I tell if my model is overfitting?

You can check for overfitting by comparing the performance of your model on training and validation datasets; a large gap indicates overfitting.