What are the consequences of log loss in machine learning?

What are the consequences of log loss in machine learning?

Log loss, or logistic loss, measures the performance of a classification model. High log loss indicates poor predictions.

How does log loss affect model evaluation?

Log loss provides a clear metric for evaluating model accuracy, especially in binary classification tasks. Lower log loss means better model performance.

Can high log loss lead to overfitting?

Yes, high log loss can indicate overfitting, where the model performs well on training data but poorly on unseen data.

What can I do to reduce log loss?

To reduce log loss, you can try feature engineering, tuning hyperparameters, or using regularization techniques.

Are there alternatives to log loss for evaluating models?

Yes, alternatives include accuracy, precision, recall, and F1 score, depending on the specific use case.