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I found that scaling in SVM (Support Vector Machine) problems really improve its performance... I have read this explanation:

"The main advantage of scaling is to avoid attributes in greater numeric ranges dominating those in smaller numeric ranges."

Unfortunately, this didn't help me ... Can somebody provide me a better explanation? Thank you in advance!

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Feature scaling is a technique used to apply to optimization problems. It means normalization of features so that its values lie under a particular range like between (0,1). If you train a model without feature scaling, then it takes time to find global minima. That’s why normalized data is used to train the model faster.

For example:


Hope this answer helps.

If you wish to know more about Acid properties and Normalization then visit, this SQL Tutorial.

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