Scikit-Learn KNeighborsClassifier Model

KNeighborsClassifier is a straightforward yet powerful classification algorithm that assigns labels based on the nearest neighbors in the feature space. It can handle both binary and multi-class classification problems by leveraging the proximity of data points.

The key hyperparameters of KNeighborsClassifier include n_neighbors (the number of neighbors to use for classification), weights (the function to weight the neighbors), and algorithm (the algorithm used to compute the nearest neighbors).

The algorithm is appropriate for classification tasks.

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

# generate synthetic 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 = KNeighborsClassifier(n_neighbors=5)

# 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 binary 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 KNeighborsClassifier model is instantiated with n_neighbors set to 5. 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 quickly set up and use a KNeighborsClassifier for binary classification tasks, showcasing the simplicity and effectiveness of this algorithm in scikit-learn.

The model can be fit directly on the training data without the need for scaling or normalization. Once fit, the model can be used to make predictions on new data, enabling its use in real-world classification problems.