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When would you use random Forest vs SVM?
Random Forest is intrinsically suited for multiclass problems, while SVM is intrinsically two-class. For multiclass problem you will need to reduce it into multiple binary classification problems. Random Forest works well with a mixture of numerical and categorical features.
Which one is better classifier support vector machine SVM or random forest RF for hyperspectral image classification and why?
Which one is better Classifier Support Vector Machine (SVM) or Random Forest (RF) for Hyperspectral Image Classification and Why? For one dataset SVM is performing better and for the other one RF is performing better.
Why SVM is good for high dimensional data?
SVM transforms the original feature space into a higher-dimensional space based on a user-defined kernel function and then finds support vectors to maximize the separation (margin) between two classes. SVM first approximates a hyperplane that separates both the classes.
Which is better for classification SVM or random forest?
For a classification problem Random Forest gives you probability of belonging to class. 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.
Which is better a random forest or a support vector machine?
Recall the table from the article about time complexity. What we can see is that the computational complexity of Support Vector Machines (SVM) is much higher than for Random Forests (RF). This means that training a SVM will be longer to train than a RF when the size of the training data is higher.
When to use a random forest vs a RF?
This means that training a SVM will be longer to train than a RF when the size of the training data is higher. This has to be considered when chosing the algorithm. Typically, SVMs tend to become unusable when the number of rows exceeds 20 000. Therefore, random forests should be prefered when the data set grows larger.
How big of a problem can a SVM solve?
Therefore, as a rule of thumb, SVM is hardly scalable beyond 10^5 points. Large number of features (homogeneous features with meaningful distance, pixel of image would be a perfect example) is generally not a problem. For a classification problem Random Forest gives you probability of belonging to class.