Log Loss
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What is Log Loss?
Log Loss, also known as cross-entropy loss, quantifies the performance of a classification model where the prediction input is a probability value between 0 and 1. It measures the uncertainty of the model's predicted probabilities compared to the actual labels. Lower Log Loss values indicate better calibrated predictions, meaning the predicted probabilities align more closely with the true outcomes.
Free resources to learn Log Loss
- Articlelog_loss | scikit-learn
- ArticleIntuition behind Log-loss Score
More in stage 26: Metrics to Evaluate
Log Loss: common questions
Can I learn Log Loss in Hindi?
This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain Log Loss in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.
Is this Log Loss tutorial for beginners?
It is lesson 103 of 150 in the free Machine Learning roadmap, in stage 26, Metrics to Evaluate. If you are new to Machine Learning, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.