Scikit-Learn HuberRegressor Model

Huber Regressor is a robust regression algorithm that is less sensitive to outliers compared to ordinary least squares regression. It combines the advantages of both linear regression and robust methods, making it suitable for datasets with some level of outlier contamination.

The key hyperparameters of HuberRegressor include epsilon (controls the point where the loss function changes from quadratic to linear), alpha (regularization strength), 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 HuberRegressor
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 = HuberRegressor()

# 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 = [[-0.67630562, -0.04153939, -0.79052302, -0.16505477, 1.11105670]]
yhat = model.predict(row)
print('Predicted: %.3f' % yhat[0])

Running the example gives an output like:

Mean Squared Error: 0.011
Predicted: -16.825

The steps are as follows:

1. First, a synthetic regression dataset is generated using the make_regression() function. This dataset includes a specified number of samples (n_samples), features (n_features), and a noise level (noise). The dataset is split into training and test sets using train_test_split().

2. Next, a HuberRegressor model is instantiated with default hyperparameters. 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 use the HuberRegressor for regression tasks, highlighting its robustness against outliers in the dataset. The model is straightforward to implement and provides an efficient way to handle data with outliers.