Phase 3 · Machine learning · Models
Loss functions
One number that says how wrong the model is.
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Why it matters
Training is optimization, and optimization needs a single number to minimize. That number is the loss, and choosing it is a real decision: squared error chases outliers, absolute error ignores them, and log loss punishes confident mistakes hard. The loss is how you tell a model what "good" means, so it should reflect what errors actually cost your business.
What you'll be able to do
- Explain what a loss function is and why training needs one
- Compute mean squared error, mean absolute error and Huber loss
- Compute log loss for probability predictions
- Choose a loss that matches what errors really cost
Before this lesson
Next in this phase: Training with gradient descent