How can I improve error rates in my machine learning model?

How can I improve error rates in my machine learning model?

To improve error rates, you can try techniques like feature engineering, hyperparameter tuning, and using more data.

What is feature engineering and how does it help?

Feature engineering involves creating new input features from your existing data, which can help the model learn better patterns.

Can you give me an example of hyperparameter tuning?

Sure! Hyperparameter tuning involves adjusting parameters like learning rate or number of trees in a random forest to optimize performance.

How do I know if my model is overfitting?

You can check for overfitting by comparing training and validation error rates; if training error is low but validation error is high, it’s likely overfitting.