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What are the problems with SVM?
What are the problems with SVM?
SumoStrength
Mon Oct 21 2024
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6 answers
1309
I'm exploring the limitations of Support Vector Machines (SVM). Could you elaborate on the common issues or problems associated with SVM, such as sensitivity to parameter settings, difficulty in interpreting results, or any other relevant challenges?
6 answers
HallyuHeroine
Wed Oct 23 2024
Additionally, SVMs require complete datasets with no missing values. If a dataset contains missing values, SVMs cannot be directly applied as they do not have a built-in mechanism to handle missing data.
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GyeongjuGlorious
Wed Oct 23 2024
For datasets with missing values, preprocessing steps such as imputation or deletion of rows with missing values may be necessary before applying SVMs. However, these steps can lead to information loss and may not always be feasible for large datasets.
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Caterina
Wed Oct 23 2024
Support Vector Machines (SVMs) are a popular class of algorithms used for classification and regression tasks. However, they have certain limitations that make them unsuitable for certain types of datasets.
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GeishaCharm
Wed Oct 23 2024
Another limitation of SVMs is that they are sensitive to the choice of kernel function and the value of the regularization parameter. Choosing the wrong kernel function or regularization parameter can significantly impact the performance of the model.
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Rosalia
Wed Oct 23 2024
Despite these limitations, SVMs are still widely used in various domains due to their excellent performance in many classification and regression tasks. However, it is important to carefully consider the characteristics of the dataset before deciding to use SVMs.
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