Standard 6 stops to get here · leads to 1

Mean Squared Error

A loss function for regression that computes the average squared difference between predictions and targets.

Your route here

6 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  3. Loss Function ✓ understood

    A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.

  4. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  5. Supervised Learning ✓ understood

    Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.

  6. Regression ✓ understood

    A supervised learning task where the model predicts continuous numerical values rather than discrete categories.

  7. Mean Squared Error · you are here ✓ understood

Where it sits

Before this

Loss Function Regression
Mean Squared Error

Leads to

Huber Loss

Explore nearby

Evaluation Mean Absolute Error The average absolute difference between predictions and actual values, a regression metric less sensitive to outliers than MSE. Training Huber Loss A loss function that's quadratic for small errors and linear for large errors, robust to outliers. Evaluation R-squared Coefficient of determination - measures the proportion of variance in the target variable explained by the model. Training Cross-Entropy Loss A loss function for classification that measures the difference between predicted and true probability distributions.