Contents
Is scaling data necessary?
Feature scaling is essential for machine learning algorithms that calculate distances between data. If not scale, the feature with a higher value range starts dominating when calculating distances, as explained intuitively in the “why?” section.
What is scaling in SVM?
Feature scaling is mapping the feature values of a dataset into the same range. Feature scaling is crucial for some machine learning algorithms, which consider distances between observations because the distance between two observations differs for non-scaled and scaled cases.
Why do you use feature scaling in SVM?
In stochastic gradient descent, feature scaling can sometimes improve the convergence speed of the algorithm. In support vector machines, it can reduce the time to find support vectors. Note that feature scaling changes the SVM result. Does this depend on the kernel? Do you recommend feature scaling on some kernels and on others not? What about:
Is the performance of SVM a drawback?
SVM performance depends on scaling and normalization. Is this considered a drawback? Unearth granular insights with advanced exploratory analytics. Dive deep and explore more with interactive features. Unearth hidden insights and grow your business. , I currently develop machine learning applications.
Which is better SVM or zero mean unit variance?
Usually, a zero mean-unit variance feature normalization (or range normalization at the very least) yields better results with the SVM. There is much research on finding the best feature scaling and shaping techniques [1,2,3] that go with an SVM.
What does scaling of data mean in machine learning?
The feature scaling just try to make the assumption that all the features has the equality opportunity to influence the weight, which more really reflects the information/knowledge you know about the data. Commonly also result in better accuracy.