What are some common mistakes in machine learning?

What are some common mistakes in machine learning?

Some common mistakes include overfitting, underfitting, not preprocessing data, and ignoring feature selection.

Can you explain overfitting and why it's a problem?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. This leads to poor performance on unseen data.

How can I avoid overfitting in my models?

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

What about data preprocessing? Why is it important?

Data preprocessing is crucial as it helps clean and transform raw data into a suitable format for modeling, improving accuracy and performance.