We would want to consider using Support Vector Machines (SVM) in the following cases:

1. When we want multiple decision boundaries with varying weights: This is not typically a strength of SVMs. SVMs are known for finding the "best" margin (or hyperplane) that separates classes, rather than multiple decision boundaries.

2. When we desire probability estimates for each class: SVMs do not inherently provide probability estimates. They are primarily used for classification and regression. However, probability estimates can be obtained in SVMs using techniques like Platt scaling, but this is not a primary feature of SVMs.

3. When we desire efficiency when using large datasets: SVMs are not particularly efficient with large datasets. The training time complexity of SVMs is more than quadratic which makes it hard to scale to dataset with more than a couple of 10000 samples.

4. When mapping the data to a higher dimensional feature space can better separate classes: This is a key strength of SVMs. They can use the kernel trick to map inputs into higher-dimensional feature spaces, making it a good choice when classes are not linearly separable in the input space.

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We would want to consider using Support Vector Machines (SVM) in the following cases:

1. When we want multiple decision boundaries with varying weights: This is not typically a strength of SVMs. SVMs are known for finding the "best" margin (or hyperplane) that separates classes, rather than multiple decision boundaries.

2. When we desire probability estimates for each class: SVMs do not inherently provide probability estimates. They are primarily used for classification and regression. However, probability estimates can be obtained in SVMs using techniques like Platt scaling, but this is not a primary feature of SVMs.

3. When we desire efficiency when using large datasets: SVMs are not particularly efficient with large datasets. The training time complexity of SVMs is more than quadratic which makes it hard to scale to dataset with more than a couple of 10000 samples.

4. When mapping the data to a higher dimensional feature space can better separate classes: This is a key strength of SVMs. They can use the kernel trick to map inputs into higher-dimensional feature spaces, making it a good choice when classes are not linearly separable in the input space.

Knowee AI · Accepted Answer

We would want to consider using Support Vector Machines (SVM) in the following cases:

1. When we want multiple decision boundaries with varying weights: This is not typically a strength of SVMs. SVMs are known for finding the "best" margin (or hyperplane) that separates classes, rather than multiple decision boundaries.

2. When we desire probability estimates for each class: SVMs do not inherently provide probability estimates. They are primarily used for classification and regression. However, probability estimates can be obtained in SVMs using techniques like Platt scaling, but this is not a primary feature of SVMs.

3. When we desire efficiency when using large datasets: SVMs are not particularly efficient with large datasets. The training time complexity of SVMs is more than quadratic which makes it hard to scale to dataset with more than a couple of 10000 samples.

4. When mapping the data to a higher dimensional feature space can better separate classes: This is a key strength of SVMs. They can use the kernel trick to map inputs into higher-dimensional feature spaces, making it a good choice when classes are not linearly separable in the input space.

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