- Scikit Learn Tutorial
- Scikit Learn - Home
- Scikit Learn - Introduction
- Scikit Learn - Modelling Process
- Scikit Learn - Data Representation
- Scikit Learn - Estimator API
- Scikit Learn - Conventions
- Scikit Learn - Linear Modeling
- Scikit Learn - Extended Linear Modeling
- Stochastic Gradient Descent
- Scikit Learn - Support Vector Machines
- Scikit Learn - Anomaly Detection
- Scikit Learn - K-Nearest Neighbors
- Scikit Learn - KNN Learning
- Classification with Naïve Bayes
- Scikit Learn - Decision Trees
- Randomized Decision Trees
- Scikit Learn - Boosting Methods
- Scikit Learn - Clustering Methods
- Clustering Performance Evaluation
- Dimensionality Reduction using PCA
- Scikit Learn Useful Resources
- Scikit Learn - Quick Guide
- Scikit Learn - Useful Resources
- Scikit Learn - Discussion
Scikit Learn - RadiusNeighborsClassifier
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 iris = 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 = RadiusNeighborsClassifier(radius = 5) 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[0]])
Output
versicolor
Complete working/executable program
from sklearn.datasets import load_iris
iris = 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)
from sklearn.neighbors import RadiusNeighborsClassifier
rnc = RadiusNeighborsClassifier(radius = 5)
rnc.fit(X_train, y_train)
classes = {0:'setosa',1:'versicolor',2:'virginicia'}
x_new = [[1,1,1,1]]
y_predict = rnc.predict(x_new)
print(classes[y_predict[0]])
