Scikit-Learn LassoLarsCV Regression Model

LassoLarsCV is a linear regression algorithm that combines least angle regression with L1 regularization. It automatically tunes the alpha parameter using cross-validation, making it suitable for regression problems where feature selection and model simplicity are important.

The key hyperparameters of LassoLarsCV include cv (number of cross-validation folds) and max_iter (maximum number of iterations).

The algorithm is appropriate for regression problems.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LassoLarsCV
from sklearn.metrics import mean_squared_error

# generate regression dataset
X, y = make_regression(n_samples=100, n_features=5, noise=0.1, random_state=1)

# split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

# create model
model = LassoLarsCV(cv=5)

# fit model
model.fit(X_train, y_train)

# evaluate model
yhat = model.predict(X_test)
mse = mean_squared_error(y_test, yhat)
print('Mean Squared Error: %.3f' % mse)

# make a prediction
row = [[-0.29435148, -0.35493357, -0.56254021, 0.59913235, 0.85762069]]
yhat = model.predict(row)
print('Predicted: %.3f' % yhat[0])

Running the example gives an output like:

Mean Squared Error: 0.010
Predicted: -6.948

The steps are as follows:

1. First, a synthetic regression dataset is generated using the make_regression() function. This creates a dataset with a specified number of samples (n_samples), features (n_features), noise level (noise), and a fixed random seed (random_state) for reproducibility. The dataset is split into training and test sets using train_test_split().

2. Next, a LassoLarsCV model is instantiated with cross-validation (cv) set to 5. The model is then fit on the training data using the fit() method.

3. The performance of the model is evaluated by comparing the predictions (yhat) to the actual values (y_test) using the mean squared error metric.

4. A single prediction can be made by passing a new data sample to the predict() method.

This example demonstrates how to quickly set up and use a LassoLarsCV model for regression tasks, showcasing the algorithm’s ability to automatically tune its regularization parameter and effectively select features.