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### Raw data outperforms Z-score transformed data in SVM classification

I've been trying to perform a binary classification using an SVM classifier (scikit-learn's SVC with RBF kernel). I have a sample size of about 100, with about 70 features each. The features are of ...
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### Why scaling is important for the linear SVM classification?

When performing the linear SVM classification, it is often helpful to normalize the training data, for example by subtracting the mean and dividing by the standard deviation, and afterwards scale ...
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### When using SVMs, why do I need to scale the features?

According to the documentation of the StandardScaler object in scikit-learn: For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support ...
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### Why does the scaling of feature vectors improve performance of SVM classifier?

I've found that performing scaling in SVM problems really improves the performance of SVM ... But I don't understand why! I have read this explanation: "The main advantage of scaling is to avoid ...
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291 views

### Scaling hinge loss in SVM

Recall the functional of primal SVM problem: $\|w\|^2 + C \sum_i \xi_i \to \min$. Suppose I have 1000 training objects, and want to find optimal $C$ by 2-fold cross-validation. As a result of cross-...
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### Why is the optimal C chosen by GridSearchCV so small?

I'm trying to use GridSearchCV to select the optimal C value in this simple SVM problem. The issue I'm having is that when I run the code the optimal C is chosen to be ridiculously small (~e-18) so ...
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### Why would scaling features decrease SVM performance?

I have used scaling on features of a model which contains 40 features (all columns are numbers) and a binary output variable. This is the Kaggle contest here I've scaled the features assuming it ...
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### Feature scaling in svm: Does it depend on the Kernel?

It is often recommended to do feature scaling (e.g. by normalization) when using a Support Vector Machine. For example here: When using SVMs, why do I need to scale the features? or also on ...
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### scaling for SVM destroys my results [duplicate]

I'm applying standard 0-1 scaling of features before SVM classification for financial data but the results are worse. This is the results before scaling ...
I have read about the Platt scaling approach to compute posterior probabilities for the SVM classifier $P(y=1|x)$. In Scikit-learn's SVC (SVM) implementation this is the approach used to produce ...