What are the advantages of SVM algorithms?

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SVM algorithms have basically advantages in terms of complexity. First I would like to clear that both Logistic regression as well as SVM can form non linear decision surfaces and can be coupled with the kernel trick. If Logistic regression can be coupled with kernel then why use SVM?

● SVM is found to have better performance practically in most cases.

● SVM is computationally cheaper O(N^2*K) where K is no of support vectors (support vectors are those points that lie on the class margin) where as logistic regression is O(N^3)

● Classifier in SVM depends only on a subset of points . Since we need to maximize distance between closest points of two classes (aka margin) we need to care about only a subset of points unlike logistic regression.

In a machine learning interview, when asked about the advantages of Support Vector Machines (SVM) algorithms, you can highlight the following points:

  1. Effective in High-Dimensional Spaces: SVMs perform well in high-dimensional spaces, making them suitable for applications in text classification, image recognition, and other domains with a large number of features.
  2. Kernel Trick: The kernel trick allows SVMs to implicitly map input data into higher-dimensional spaces. This can be beneficial when dealing with non-linearly separable data, as SVMs can transform the data to a space where a linear decision boundary can be applied.
  3. Robust to Overfitting: SVMs are less prone to overfitting, especially in high-dimensional spaces, compared to other algorithms. This makes them effective when dealing with small to medium-sized datasets.
  4. Global Optimization: SVMs aim to find the hyperplane that maximally separates classes, making the solution more globally optimal compared to local optimization methods.
  5. Memory Efficient: SVMs use a subset of training points (support vectors) in the decision function, making them memory-efficient, particularly when dealing with large datasets.
  6. Versatility in Kernels: SVMs support different kernel functions (linear, polynomial, radial basis function, etc.), providing flexibility to adapt to various data distributions.
  7. Works Well in Both Linear and Non-Linear Cases: SVMs are effective in linearly separable as well as non-linearly separable cases due to the ability to use different kernels.
  8. Tuneable Parameters: SVMs offer parameters such as C (regularization parameter) and the choice of kernel, allowing practitioners to fine-tune the model for better performance.
  9. Effective in Small and Medium-sized Datasets: SVMs are particularly suitable for situations where the number of features is high, and the dataset is not too large.
  10. Wide Range of Applications: SVMs are used in various fields such as image classification, bioinformatics, handwriting recognition, and financial forecasting, showcasing their versatility.

When discussing these advantages, it’s beneficial to provide examples or real-world applications to illustrate how SVMs can be practically applied in different scenarios.