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To mention some disadvantages, SVM models could be very calculation intensive while training the model and they do not return a numerical indicator of how confident they are about a prediction. However, we can use some techniques such as K-fold cross-validation to avoid this, at the cost of increasing the computational cost.

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Cover of Learning scikit-learn: Machine Learning in Python

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SVM disadvantages