do values need to normalized for lasso to work properly? in python

Before applying Lasso regression, it is important to normalize the feature values. Normalization is necessary in order to ensure that all features are on a similar scale and to prevent one feature from dominating the others due to its larger magnitude.

In Python, you can normalize the feature values using the StandardScaler class from the sklearn.preprocessing module. Here's an example:

main.py
from sklearn.preprocessing import StandardScaler

# Assuming X is your feature matrix
scaler = StandardScaler()
X_normalized = scaler.fit_transform(X)
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In the code above, the fit_transform() method of the StandardScaler class scales and normalizes the feature matrix X. This step ensures that all features have zero mean and unit variance.

After normalizing the feature values, you can then proceed with fitting the Lasso regression model using the normalized features:

main.py
from sklearn.linear_model import Lasso

lasso = Lasso(alpha=0.1)  # Specify the desired regularization parameter
lasso.fit(X_normalized, y)  # Train the Lasso regression model
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By normalizing the feature values, you ensure that the Lasso regression algorithm works properly and that the regularization term can be applied fairly to all features.