Support Vector Machine vs K Nearest Neighbours
|
Aspect |
Support Vector Machine(SVM) |
K Nearest Neighbour(KNN) |
|---|---|---|
|
Basis |
Based on optimal hyperplane that maximizes the margin between classes. |
Classify a data point based on the majority class of its nearest neighbours. |
|
Algorithm Type |
Discriminative algorithm, learn by analyzing the differences between classes. |
Lazy Learning, stores all the training data and classifies new points based on their similarity to the stored data points at prediction time. |
|
Training Type |
Slower |
Faster as there is no explicit learning |
|
Decision Boundary |
Can be either linear or non-linear |
Non-linear |
|
Memory Usage |
Lower |
Flexible, adapts to data distribution |
|
Prediction Time |
Faster |
Slower with large dataset |
|
Performance with Imbalanced Data |
Handles well |
May or may not perform well |
|
Hyperparameter Sensitivity |
Very less parameters |
More parameters depending on the value of K |
|
Interpretability |
Less interpretable,due to the complex nature of their decision boundaries |
More interpretable,because the classification relies on the nearest neighbors. |
|
Scalability |
More scalable |
Less scalable |
|
Example |
Text Classification, Image classification, Bioinformatics, etc. |
Recommendation Systems, Anomaly Detection, Predictive maintenance, etc. |
SVM vs KNN in Machine Learning
Support Vector Machine(SVM) and K Nearest Neighbours(KNN) both are very popular supervised machine learning algorithms used for classification and regression purpose. Both SVM and KNN play an important role in Supervised Learning.
Table of Content
- Support Vector Machine(SVM)
- K Nearest Neighbour(KNN)
- Support Vector Machine vs K Nearest Neighbours