What are Support Vector Machines?
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Welcome to this introductory video tutorial lesson on SVMs (Support Vector Machines)- a type of supervised machine learning algorithm which can be used for classification and regression purposes.
In the case of supervised classification models, we aim to arrange the objects in two or more categories. For example, if we have images of different domestic animals like cats, dogs and horses then we would want to implement an algorithm that can identify which animal appears in a specific image with a high degree of certainty.
In addition classification can be used for simple yes or no tasks such as predicting whether an online bank transaction is fraudulent thanks to information from past behavior. The bank has data on the amount of money a client typically withdraws the time of day they do so and their primary location. So based on this data, an algorithm can be trained to assess the likelihood of fraudulent transactions in such a scenario.
The purpose of a classification algorithm is to divide the different classes in the most optimal way possible. Support vector machines tackle the issue by creating a hyperplane that maximizes the distance between the classes.
The values we are predicting are not limited to categories. We want to predict real numbers when we use characteristics such as age, work experience and skills to predict the salaries of a group of people based on data about other similar to them. We use regression analysis here and the support vectors fit the data again using a hyper plane. The difference her is that the algorithm is not looking for an exact match, instead allowing some points to be misclassified in order to reach an overall good solution without overfitting the data.
Watch the rest of the video to learn how support vector machines can solve non-liner problems with the kernel trick, as well as the advantages and disadvantages of these models.
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