What is regression in machine learning?

02 · See it in action

Follow the steps

1. Create a small synthetic input and numeric target.
2. Fit a linear regression model.
3. Compare predictions with held-out observations.
4. Inspect residuals, the difference between observed and predicted values.

Worked example

from sklearn.linear_model import LinearRegression
x = [[1], [2], [3], [4]]
y = [3, 5, 7, 9]
model = LinearRegression().fit(x, y)
print(model.predict([[5]]))  # approximately [11.]

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