Least Angle Regression (LARS) in python

Least Angle Regression (LARS) in python

Least Angle Regression (LARS) is a regression algorithm that is particularly useful when you have more features than observations. It is also efficient in scenarios where the solution is sparse (i.e., when only a few predictors have non-zero coefficients). In Python, you can perform LARS using the Lars class from the sklearn.linear_model module in the scikit-learn library.

Here is an example of how to use LARS in Python:

First, you need to install scikit-learn if you haven't already:

pip install scikit-learn

Then, you can use the following code to apply LARS to a dataset:

from sklearn import linear_model
from sklearn.datasets import make_regression

# Generate some sparse sample data
X, y = make_regression(n_samples=200, n_features=500, n_informative=10, noise=2)

# Fit the LARS model
lars = linear_model.Lars(n_nonzero_coefs=10)
lars.fit(X, y)

# Get the coefficients
print("Coefficients obtained by LARS:")
print(lars.coef_)

# Predict new data
predicted = lars.predict(X)

# You can also check how many iterations the algorithm ran before converging
print("Number of iterations:", lars.n_iter_)

In this example:

  • make_regression() is used to generate a synthetic regression problem with a large number of features, but only some of them are informative.
  • Lars() initializes the LARS model, where n_nonzero_coefs specifies the maximum number of non-zero coefficients in the model. If set to np.inf, LARS will perform a full least squares optimization.
  • fit() fits the LARS model to the data.
  • predict() is used to predict the output for the given input data.

Remember that LARS is sensitive to the scaling of the input features. It's common to standardize features before applying LARS by subtracting the mean and dividing by the standard deviation for each feature. Scikit-learn provides a convenient utility called StandardScaler for this purpose.

Also, consider using cross-validation to select an optimal number of non-zero coefficients if you do not have prior knowledge about the sparsity of the solution. Scikit-learn provides LarsCV for this purpose, which performs Least Angle Regression with built-in cross-validation of the alpha parameter.

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