Lasso Regression


# Load library
from sklearn.linear_model import Lasso
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler

Load Boston Housing Dataset

# Load data
boston = load_boston()
X =
y =

Standardize Features

# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

Fit Ridge Regression

The hyperparameter, $\alpha$, lets us control how much we penalize the coefficients, with higher values of $\alpha$ creating simpler modelers. The ideal value of $\alpha$ should be tuned like any other hyperparameter. In scikit-learn, $\alpha$ is set using the alpha parameter.

# Create lasso regression with alpha value
regr = Lasso(alpha=0.5)

# Fit the linear regression
model =, y)