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Which model is more accurate SVM or logistic regression?
SVM try to maximize the margin between the closest support vectors whereas logistic regression maximize the posterior class probability. SVM is deterministic (but we can use Platts model for probability score) while LR is probabilistic. For the kernel space, SVM is faster.
Why is SVM more accurate than logistic regression?
SVM tries to finds the “best” margin (distance between the line and the support vectors) that separates the classes and this reduces the risk of error on the data, while logistic regression does not, instead it can have different decision boundaries with different weights that are near the optimal point.
Can we use kernel in logistic regression?
Kernel logistic regression is a technique that extends regular logistic regression to deal with data that is not linearly separable. Kernel logistic regression requires you to specify a kernel function and parameters for the kernel function. The demo uses a radial basis function (RBF) kernel function.
What is a kernel explain the Kernel Trick?
A Kernel Trick is a simple method where a Non Linear data is projected onto a higher dimension space so as to make it easier to classify the data where it could be linearly divided by a plane.
Which is better linear SVM or logistic regression?
Linear SVMs and logistic regression generally perform comparably in practice. Use SVM with a nonlinear kernel if you have reason to believe your data won’t be linearly separable (or you need to be more robust to outliers than LR will normally tolerate). Otherwise, just try logistic regression first and see how you do with that simpler model.
When to use linear vs Guassian kernel with SVM?
Simply speaking, one may want to use SVM with linear kernel when data distribution is linearly separable. In scenarios, where there are smaller number of features and large number of training examples, one may use what is called Gaussian Kernel.
What are the advantages and disadvantages of SVM?
As is known to all, SVM can use kernel method to project data points in higher spaces so that points can be separated by a linear space. But we can also use logistic regression to choose this boundary in the kernel space, so what’s the advantages of SVM?
How are support vector machines and logistic regression related?
• Logistic regression and support vector machines are closely linked. • Both can be viewed as taking a probabilistic model and minimizing some cost associated with misclassification based on the likelihood ratio. • This lets us analyze these classifiers in a decision theoretic framework.