Overview
k-Nearest Neighbors (KNN) is a simple and effective supervised learning algorithm used for classification and regression tasks. In this lesson, we’ll explore the fundamentals of KNN, its working principles, implementation in Python using Scikit-Learn, practical considerations, and applications.
Learning Objectives
- Understand the concept and advantages of k-Nearest Neighbors.
- Implement KNN for classification tasks using Python.
- Explore practical considerations, distance metrics, and considerations for KNN.
What is k-Nearest Neighbors (KNN)?
KNN is a non-parametric and instance-based learning algorithm. It makes predictions by comparing new data points with known data points (training data) and assigns the class label or numerical value based on the majority (for classification) or average (for regression) of its k nearest neighbors.
How k-Nearest Neighbors Works
KNN operates by:
- Distance Calculation: Measures the distance (typically Euclidean distance) between the query instance and all training samples.
- K Neighbors: Selects the k nearest neighbors based on the smallest distance.
- Majority Voting: For classification, assigns the class label by majority voting among the k neighbors. For regression, computes the average of the k nearest neighbors.
Implementing KNN in Python
Here’s how you can implement KNN using Python’s Scikit-Learn library for a classification task:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, classification_report
# Load dataset
iris = load_iris()
X = iris.data
y = iris.target
# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Initialize and train KNN Classifier
model = KNeighborsClassifier(n_neighbors=5)
model.fit(X_train, y_train)
# Predictions
y_pred = model.predict(X_test)
# Evaluation
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')
# Classification report
print('Classification Report:')
print(classification_report(y_test, y_pred, target_names=iris.target_names))
Practical Considerations
- Choosing k: Optimal value of k affects model performance; typically chosen via cross-validation.
- Distance Metric: Euclidean distance is common, but other metrics like Manhattan, Minkowski, or custom-defined can be used based on the problem domain.
- Feature Scaling: KNN is sensitive to feature scaling, so it’s beneficial to normalize or standardize features.
Applications and Limitations
- Applications: KNN is used in recommendation systems, anomaly detection, and image recognition.
- Limitations: Computationally expensive for large datasets due to calculating distances for each query point. Sensitivity to irrelevant or noisy features can affect performance.
Conclusion
K-Nearest Neighbors is a straightforward yet powerful algorithm for classification and regression tasks. By implementing KNN in Python, understanding distance metrics, tuning parameters like k, and considering practical applications and limitations, you can effectively apply KNN to various machine learning problems.