Scikit-Learn RadiusNeighborsClassifier Model

RadiusNeighborsClassifier is a classification algorithm that determines the class of a data point based on the number of neighbors within a specified radius. This instance-based learning method is effective for classification tasks where the local structure of the data is important.

The key hyperparameters for RadiusNeighborsClassifier include radius, which defines the size of the neighborhood used for classification, and weights, which determines the weight function used in prediction (e.g., uniform or distance-based weights).

This algorithm is suitable for classification problems.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.neighbors import RadiusNeighborsClassifier
from sklearn.metrics import accuracy_score

# generate synthetic classification dataset
X, y = make_classification(n_samples=100, n_features=5, n_classes=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 = RadiusNeighborsClassifier(radius=2.0)

# fit model
model.fit(X_train, y_train)

# evaluate model
yhat = model.predict(X_test)
acc = accuracy_score(y_test, yhat)
print('Accuracy: %.3f' % acc)

# make a prediction
row = [[-1.10325445, -0.49821356, -0.05962247, -0.89224592, -0.70158632]]
yhat = model.predict(row)
print('Predicted: %d' % yhat[0])

Running the example gives an output like:

Accuracy: 0.950
Predicted: 0

The steps are as follows:

1. First, a synthetic classification dataset is generated using the make_classification() function. This creates a dataset with a specified number of samples (n_samples), classes (n_classes), 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 RadiusNeighborsClassifier model is instantiated with the radius hyperparameter set to 2.0. 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 accuracy score 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 RadiusNeighborsClassifier for classification tasks, showcasing its simplicity and effectiveness in scikit-learn. The model can be used directly on the training data, and once fit, it can make predictions on new data samples.