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:
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 usingtrain_test_split()
.Next, a
LassoLarsCV
model is instantiated with cross-validation (cv
) set to 5. The model is then fit on the training data using thefit()
method.The performance of the model is evaluated by comparing the predictions (
yhat
) to the actual values (y_test
) using the mean squared error metric.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.