Support Vector Machines | Supervised Learning | Day (7/45) | A2Z ML | Mohd Saqib

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SVM could be more useful than Naive Bayes (NB) or Decision Trees (DT) is in text classification tasks with highly complex and overlapping classes, such as sentiment analysis of product reviews for nuanced categories like “very negative,” “slightly negative,” “neutral,” “slightly positive,” and “very positive.”

SVM is highly effective in dealing with data that is not linearly separable due to its ability to find a hyperplane that maximizes the margin between classes. This is especially useful in sentiment analysis, where positive and negative sentiments may overlap significantly in the feature space.

Text data, represented as word embeddings or TF-IDF vectors, results in high-dimensional feature spaces. SVMs are well-suited to high-dimensional datasets and can perform well even when the number of features is much greater than the number of samples.

Unlike Naive Bayes, which assumes independence between features, SVM does not rely on this assumption and can capture more complex patterns in data, especially with fewer samples. For text classification in niche domains with limited labeled data, SVM often provides more accurate results.

Decision Trees can sometimes overfit noisy data unless pruned. SVM, by contrast, tends to be more robust to noise, especially with the use of kernels and soft margins, making it better…