Linear regression in Python

Fitting a line, then reading the coefficients and the error.

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

X = [[1.0], [2.0], [3.0], [4.0], [5.0]]
y = [2.1, 3.9, 6.2, 7.8, 10.1]

model = LinearRegression().fit(X, y)
predicted = model.predict(X)

print(f"slope {model.coef_[0]:.2f} intercept {model.intercept_:.2f}")
print(f"mse {mean_squared_error(y, predicted):.3f}")
print(f"r2 {r2_score(y, predicted):.3f}")

How it works

1. coef_ holds one weight per feature, intercept_ the offset.
2. mean_squared_error and r2_score describe the fit.
3. Predictions come back as an array, one per row of X.

Keywords and builtins used here

• print

The run, in numbers

Lines

12

Characters to type

411

Tokens

129

Three-star pace

100 tpm

At the three-star pace of 100 tokens a minute, this run takes about 77 seconds.

Type this snippet

Step 3 of 3 in The estimator API, step 3 of 25 in Machine learning with scikit-learn.

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Linear regression in other languages

• R