Machine Learning Basics·Machine learning from data to a responsible model

Regression

Predict a continuous value with linear regression and understand its assumptions.

Intermediate 15 min read Lesson 4 of 300 Challenge included

What you will be able to do

Use LinearRegression as the first baseline.
Inspect coefficients with feature names.
Check residuals for obvious patterns.
Use regularisation when features are many or correlated.

In plain English

Linear regression fits a line that minimises squared error. It is fast, interpretable and a strong baseline. Coefficients describe direction and size but not causation.

regression.pyPython

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression().fit(X_train, y_train)
pred = model.predict(X_test)
print(mean_absolute_error(y_test, pred))

Your turn

Train a baseline

Easy

Fit a LinearRegression on the training split and predict the test split.

PYTHON · starter

model = LinearRegression()

Check your understanding before moving on.

Knowledge checkQuestion 1

What does a linear regression coefficient mean?

Select an answer to continue

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Lesson 4 of 300 · 0% of course