Scikit-Learn RadiusNeighborsRegressor Model

RadiusNeighborsRegressor is a regression algorithm that uses the distance within a fixed radius to predict continuous values.

The key hyperparameters of RadiusNeighborsRegressor include radius (the radius of the neighborhood) and weights (the weight function used in prediction).

This algorithm is suitable for regression problems where the relationship between features and target is non-linear and the data points are unevenly distributed.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.neighbors import RadiusNeighborsRegressor
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 = RadiusNeighborsRegressor(radius=2.0, weights='distance')

# 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 = [[-0.76953065, 1.24643474, 0.50875819, -1.63218452, 0.58881019]]
yhat = model.predict(row)
print('Predicted: %.3f' % yhat[0])

Running the example gives an output like:

Mean Squared Error: 1370.698
Predicted: 46.141

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 RadiusNeighborsRegressor model is instantiated with a radius of 2.0 and weights set to 'distance'. 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 RadiusNeighborsRegressor model for regression tasks, showcasing the algorithm’s ability to handle non-linear relationships and unevenly distributed data points in scikit-learn.