The Radius in the name of this classifier represents the nearest neighbors within a specified radius r, where r is a floating-point value specified by the user. Hence as the name suggests, this classifier implements learning based on the number neighbors within a fixed radius r of each training point. Let’s understand it more with the help if an implementation example −

## Implementation Example

In this example, we will be implementing KNN on data set named Iris Flower data set by using scikit-learn RadiusNeighborsClassifer

First, import the iris dataset as follows −

from sklearn.datasets import load_iris


Now, we need to split the data into training and testing data. We will be using Sklearn train_test_split function to split the data into the ratio of 70 (training data) and 20 (testing data) −

X = iris.data[:, :4]
y = iris.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)


Next, we will be doing data scaling with the help of Sklearn preprocessing module as follows −

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)


Next, import the RadiusneighborsClassifier class from Sklearn and provide the value of radius as follows −

from sklearn.neighbors import RadiusNeighborsClassifier
rnc.fit(X_train, y_train)


## Example

Now, create and predict the class of two observations as follows −

classes = {0:'setosa',1:'versicolor',2:'virginicia'}
x_new = [[1,1,1,1]]
y_predict = rnc.predict(x_new)
print(classes[y_predict])


## Output

versicolor


## Complete working/executable program

from sklearn.datasets import load_iris
X = iris.data[:, :4]

y = iris.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)