Landmark 2 stops to get here · leads to 6

Regression

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

Your route here

2 stops · basics first
  1. 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.

  2. Supervised Learning ✓ understood

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

  3. Regression · you are here ✓ understood

Picture it

Regression

  • Predicts a continuous number
  • e.g. house price: $412,500
  • Loss: mean squared error
  • Judged by RMSE or R²

Classification

  • Predicts a discrete category
  • e.g. spam or not spam
  • Loss: cross-entropy
  • Judged by accuracy, precision, recall
Notice the only real difference is the output: regression answers "how much?" while classification answers "which one?"

Where it sits

Before this

Supervised Learning
Regression

Leads to

Decision Tree Mean Squared Error Gaussian Process Mean Absolute Error R-squared Time Series Forecasting

Explore nearby

Foundations Classification A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam. Training Mean Squared Error A loss function for regression that computes the average squared difference between predictions and targets. Evaluation R-squared Coefficient of determination - measures the proportion of variance in the target variable explained by the model. Foundations Decision Tree A tree-structured model that makes decisions by splitting data based on feature values, interpretable but prone to overfitting. Foundations Time Series Forecasting Predicting future values based on historical sequential data, using models like ARIMA, LSTMs, or Transformers.