Scikit-Learn LinearSVR Model

LinearSVR is a support vector machine algorithm for regression that aims to find a hyperplane that best fits the data while minimizing the error margin.

The key hyperparameters of LinearSVR include C (regularization parameter), epsilon (specifies the epsilon-tube within which no penalty is associated in the training loss function), and loss (specifies the loss function to be used).

The algorithm is appropriate for regression tasks where the goal is to predict continuous values.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVR
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 = LinearSVR()

# 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, -1.2, 1.3, -0.7, 2.0]]
yhat = model.predict(row)
print('Predicted: %.3f' % yhat[0])

Running the example gives an output like:

Mean Squared Error: 188.615
Predicted: 10.893

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), 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 LinearSVR 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 quickly set up and use a LinearSVR model for regression tasks, showcasing the simplicity and effectiveness of this algorithm in scikit-learn.