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In which scenario would you prefer using SVM over other classification algorithm
In which scenario would you prefer using SVM over other classification algorithms?
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Q1
In which scenario would you prefer using SVM over other classification algorithms?
When the dataset is very large
When the data is linearly separable
When the data has a high dimensionality
When the data is highly imbalanced
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SVM is particularly effective in high-dimensional spaces, making it suitable for datasets with many features.
Questions & Step-by-step Solutions
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Q
Q: In which scenario would you prefer using SVM over other classification algorithms?
Solution:
SVM is particularly effective in high-dimensional spaces, making it suitable for datasets with many features.
Steps: 5
Show Steps
Step 1: Understand what SVM (Support Vector Machine) is. It is a type of algorithm used for classification tasks.
Step 2: Identify the characteristics of your dataset. Check if it has a lot of features (high-dimensional space).
Step 3: Consider the performance of other classification algorithms on your dataset. Some may struggle with many features.
Step 4: If your dataset has many features and other algorithms are not performing well, think about using SVM.
Step 5: Remember that SVM works well when the classes are well-separated in high-dimensional spaces.
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