Linked Questions

0 votes
2 answers

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 ...
Nitram's user avatar
  • 3
3 votes
1 answer

What's the score employed by Platt scaling to compute SVM posterior probabilities?

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 ...
SkyWalker's user avatar
  • 925
7 votes
1 answer

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 ...
Ferdi's user avatar
  • 5,197
1 vote
1 answer

Why is SVM sensitive to scaling of features? [duplicate]

In the formulation of SVM I don't see any step which states that scaling of features should be done for better generalization performance, nor does it come up in its VC dimension expression. So why is ...
Siddharth Shakya's user avatar
6 votes
3 answers

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 ...
Shovalt's user avatar
  • 218
4 votes
1 answer

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 ...
Krzysztof Fajst's user avatar
12 votes
2 answers

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 ...
scallywag's user avatar
  • 123
5 votes
2 answers

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 ...
Kevin's user avatar
  • 245
2 votes
2 answers

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-...
esokolov's user avatar
9 votes
1 answer

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 ...
mahonya's user avatar
  • 1,111
20 votes
2 answers

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 ...
Qinghua's user avatar
  • 201