Scikit-Learn Lasso Regression Model

Lasso (Least Absolute Shrinkage and Selection Operator) is a regression analysis method that performs both variable selection and regularization to enhance prediction accuracy and interpretability. It is particularly useful when dealing with datasets that have a large number of features.

The key hyperparameters of Lasso include the alpha (constant that multiplies the L1 term), max_iter (maximum number of iterations), and tol (tolerance for the optimization).

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 Lasso
from sklearn.metrics import mean_squared_error

# generate synthetic regression dataset
X, y = make_regression(n_samples=100, n_features=20, 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 Lasso model
model = Lasso(alpha=1.0)

# 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 = [[0.5]*20]
yhat = model.predict(row)
print('Predicted: %.3f' % yhat[0])

Running the example gives an output like:

Mean Squared Error: 18.179
Predicted: 228.459

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 Lasso model is instantiated with the default alpha value of 1.0. The model is then fit on the training data using the fit() method.

3. The performance of the model is evaluated by predicting on the test set and computing the Mean Squared Error (MSE) using mean_squared_error().

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 Lasso model for regression tasks, showcasing the model’s ability to perform regularization and variable selection to improve prediction accuracy.