What are negative weights in machine learning?

What are negative weights in machine learning?

Negative weights in machine learning refer to the coefficients that can decrease the output of a model. They indicate an inverse relationship between the input feature and the target variable.

Can you give me an example of how negative weights work?

Sure! In a linear regression model, if a feature like 'temperature' has a negative weight, it means that as temperature increases, the predicted outcome (like ice cream sales) decreases.

Are negative weights common in all types of models?

Not necessarily. Negative weights are more common in models that allow for both positive and negative relationships, like linear regression or neural networks, but may not appear in all algorithms.

How do negative weights affect model performance?

Negative weights can improve model performance by capturing complex relationships in the data, but they can also lead to overfitting if not managed properly.