Why is SVM better than others
SVMs have several advantages, such as the ability to handle high-dimensional data and the ability to perform well with small datasets. They also have the ability to model non-linear decision boundaries, which can be very useful in many applications.
What is the advantage of SVM
The advantages of SVM and support vector regression include that they can be used to avoid the difficulties of using linear functions in the high-dimensional feature space, and the optimization problem is transformed into dual convex quadratic programs.
Why is SVM more accurate
In SVM, the data is classified into two classes and the hyper plane lies between those two classes. The advantage of SVM is that it also considers data being close to the opposite class and thus gives a reliable classification.
Why is SVM better than kNN
While both algorithms yield positive results regarding the accuracy in which they classify the images, the SVM provides significantly better classification accuracy and classification speed than the kNN.
Why does SVM perform better than Naive Bayes
Even though, NB gives good results when applied to short texts like tweets. For some datasets, NB may defeat other classifiers using feature selection. SVM is more powerful to address non-linear classification tasks. SVM generalizes well in high dimensional spaces like those corresponding to texts.
Why SVM is better than decision tree
SVM works better with large amount of data where there is more input training data. It can also fit any data changes because of n-dimensional classification. Easy to scale to large datasets. It is powerful in learning complicated rules and efficient in performance.
Why SVM is better than logistic
SVM has kernel methods which can classify features by mapping data in higher dimensions using orthogonal projections and RBF kernels. Since SVM can handle complex data, there would be less room for errors compared to Logistic Regression.
Why does SVM perform better than random forest
It is because in this dataset, data is sparse and easy to classify, hence SVM works faster and provides better results. However, random forest also gives good results but does not match upto SVM for this particular dataset. The choice of algorithm depends upon the desired outcome.
Is SVM always better than Naive Bayes classifier
So if you have interactions, and, given your problem, you most likely do, an SVM will be better at capturing those, hence better at the classification task you want. The consensus for ML researchers and practitioners is that in almost all cases, the SVM is better than the Naive Bayes.
Why SVM performs better than Random Forest
SVM gives you distance to the boundary, you still need to convert it to probability somehow if you need probability. For those problems, where SVM applies, it generally performs better than Random Forest. SVM gives you "support vectors", that is points in each class closest to the boundary between classes.
Why is SVM more accurate than logistic
Unlike logistic regression, SVMs are designed to generate more complex decision boundaries. An LS-SVM with a simple linear kernel function corresponds to a linear decision boundary. Instead of a linear kernel, more complex kernel functions, such as the commonly used RBF kernel, can be chosen.
