# Simple support vectors question

• Femme_physics
In summary, Dory confirms that the reaction vectors are correct and suggests reversing the direction of the horizontal vector if it appears to be drawn backwards. She also mentions that the negative sign in the numeric result indicates a backwards direction. The person is grateful for the clarification but admits they still do not fully understand the concept.
Femme_physics
Gold Member
Are my reaction vectors correct?

Truth be told I'm not exactly sure what am I looking it... but did I get it right?

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Dory: Yes, nice work. Your reaction vectors look correct (if mu = 0). However, it currently looks like your horizontal vector might be drawn backwards. Therefore, you might want to reverse its direction. If it is drawn backwards, then the numeric result for that vector will have a negative sign, telling you the direction of that vector is drawn backwards.

Last edited:
Ah, good, thanks... wasn't sure about the one in the middle. Still no idea what I'm looking at, heh.

## What is a support vector?

A support vector is a data point that falls closest to the decision boundary of a classification model. It is used to define the margin of separation between different classes of data.

## What is the purpose of support vectors in machine learning?

The purpose of support vectors in machine learning is to help determine the decision boundary of a classification model and improve its accuracy. They are also used to identify the most important features of a dataset and reduce the dimensionality of the data.

## How are support vectors identified?

Support vectors are identified by finding the data points that are closest to the decision boundary of a classification model. This process is known as the margin maximization algorithm.

## What is a soft margin in support vector machines?

A soft margin in support vector machines is a way to allow for some misclassified data points in order to create a more flexible decision boundary. This is useful when dealing with datasets that are not perfectly separable.

## What are the advantages of using support vector machines?

Support vector machines have several advantages, including their ability to handle high-dimensional data, their effectiveness in dealing with non-linearly separable data, and their ability to handle large datasets. They also have good generalization performance and are less prone to overfitting compared to other machine learning algorithms.

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