Scikit-Learn TheilSenRegressor Model

Theil-Sen Regression is a robust linear regression algorithm that is resistant to outliers. It is particularly useful when the dataset contains a significant amount of noise or outliers.

Key hyperparameters include:

max_subpopulation: the maximum number of subpopulations considered in the estimator.
n_subsamples: the number of samples to draw.
max_iter: maximum number of iterations for the random sampling.
tol: tolerance to declare convergence.

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 TheilSenRegressor
from sklearn.metrics import mean_absolute_error

# generate regression dataset
X, y = make_regression(n_samples=100, n_features=5, noise=0.2, 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 = TheilSenRegressor()

# fit model
model.fit(X_train, y_train)

# evaluate model
yhat = model.predict(X_test)
mae = mean_absolute_error(y_test, yhat)
print('Mean Absolute Error: %.3f' % mae)

# make a prediction
row = [[0.5, -1.2, 0.3, 0.8, 1.5]]
yhat = model.predict(row)
print('Predicted: %.3f' % yhat[0])

Running the example gives an output like:

Mean Absolute Error: 0.191
Predicted: 5.554

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), and a fixed random seed (random_state) for reproducibility. The dataset is split into training and test sets using train_test_split().
Next, a TheilSenRegressor model is instantiated with default hyperparameters. The model is then fit on the training data using the fit() method.
The performance of the model is evaluated by comparing the predictions (yhat) to the actual values (y_test) using the mean absolute error metric.
A single prediction can be made by passing a new data sample to the predict() method.

This example demonstrates how to set up and use a TheilSenRegressor model for regression tasks, highlighting the robustness of this algorithm in handling datasets with outliers and noise.

See Also