Cryptocurrency Q&A What are the disadvantages of SVM?

What are the disadvantages of SVM?

KDramaLegendaryStar KDramaLegendaryStar Fri Sep 13 2024 | 5 answers 941
Can you elaborate on the drawbacks of using Support Vector Machines (SVM) in financial analysis and cryptocurrency predictions? Are there any limitations in terms of scalability, interpretability, or their ability to handle non-linear relationships effectively? Additionally, how might SVM's sensitivity to hyperparameters and the choice of kernel function affect the accuracy and stability of predictions in the volatile cryptocurrency market? Lastly, are there more suitable machine learning algorithms for this domain, and if so, why? What are the disadvantages of SVM?

5 answers

GinsengGlory GinsengGlory Sun Sep 15 2024
Another challenge that SVM encounters is handling noisy data. When the data set contains a significant amount of noise, such as overlapping target classes, SVM's classification accuracy can suffer. This limitation highlights the need for preprocessing techniques to clean and refine the data before applying SVM.

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KpopMelody KpopMelody Sun Sep 15 2024
The SVM algorithm, despite its widespread use in various machine learning applications, faces limitations when dealing with large data sets. Its performance can deteriorate significantly as the size of the dataset increases, making it less suitable for processing massive amounts of information.

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CryptoChampion CryptoChampion Sat Sep 14 2024
Additionally, SVM's performance is also impacted by the ratio of features to training data samples. In scenarios where the number of features for each data point exceeds the number of training data samples, SVM tends to underperform. This issue underscores the importance of feature selection and dimensionality reduction in preparing the data for SVM analysis.

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Luca Luca Sat Sep 14 2024
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CherryBlossomFall CherryBlossomFall Sat Sep 14 2024
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