What is loss?
Loss is a number that tells us how far off a computer’s prediction is from the actual answer. In machine learning, the model makes a guess, compares it to the true value, and the loss measures the difference. A smaller loss means the model is doing a better job.
Let's break it down
- Prediction: The model’s output (e.g., “the house will cost $300k”).
- True value: The real answer (e.g., the house actually costs $320k).
- Error: The gap between prediction and true value.
- Loss function: A formula that turns the error into a single number (like squaring the difference).
- Training: The model adjusts its internal settings to make the loss as low as possible.
Why does it matter?
Loss is the feedback signal that guides learning. Without it, the model wouldn’t know whether it’s improving or getting worse. By continuously minimizing loss, the model becomes more accurate and reliable for tasks like image recognition, language translation, or recommendation systems.
Where is it used?
- Image classification (e.g., recognizing cats vs. dogs)
- Speech recognition (turning spoken words into text)
- Predicting stock prices or weather
- Recommending movies or products
- Any AI system that learns from data and needs to improve its predictions
Good things about it
- Provides a clear, quantitative way to measure performance.
- Works with many different types of problems (regression, classification, etc.).
- Enables automatic optimization using algorithms like gradient descent.
- Helps compare different models or settings objectively.
Not-so-good things
- Choosing the wrong loss function can lead to poor results (e.g., using mean squared error for classification).
- Some loss functions are hard to compute or may cause training to be unstable.
- Over‑optimizing loss on training data can cause overfitting, where the model performs badly on new data.
- Interpreting loss values isn’t always intuitive for non‑technical users.