Scikit-Learn PassiveAggressiveRegressor Model

PassiveAggressiveRegressor is a linear model for regression problems that is particularly useful for large datasets and streaming or online learning scenarios. It iteratively adjusts its parameters to minimize the loss on the data it has seen so far, making it efficient for situations requiring quick updates.

The key hyperparameters of PassiveAggressiveRegressor include C (regularization term), epsilon (insensitivity parameter), and max_iter (maximum number of passes over the training data).

The algorithm is appropriate for regression tasks.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.linear_model import PassiveAggressiveRegressor
from sklearn.metrics import mean_squared_error

# generate a 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 = PassiveAggressiveRegressor(max_iter=1000, random_state=1)

# 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, -0.2, 1.5, 0.3, 0.1]]
yhat = model.predict(row)
print('Predicted: %.3f' % yhat[0])

Running the example gives an output like:

Mean Squared Error: 0.015
Predicted: 57.139

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 PassiveAggressiveRegressor model is instantiated with max_iter set to 1000 and a fixed random seed. 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 set up and use a PassiveAggressiveRegressor for regression tasks, highlighting the model’s ability to handle large datasets and perform online learning efficiently.