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Support Vector Machine Disadvantages

Many Options for Customization

1- A State of Art Model with lots of Bells and Whistles

In this article we will elaborate on disadvantages of Support Vector Machines that can potentially be limiting in practical use. We will try to present a critical case which shouldn’t render Support Vector Machines completely pointless or unpopular. SVMs are still very useful Machine Learning algorithms, they are being used by many experts, scientists, business professional, engineers and other Machine Learning practitioners.

Despite a number of disadvantages SVMs have many fans. Knowing the disadvantages and pitfalls of SVMs programmers and data scientists can avoid unexpected results and disappointments.  One thing with Support Vector Machines is you will really want to know how to optimize them. See tutorial below:

As Machine Learning projects naturally require many iterations it can be very beneficial to avoid wrong model selection in the beginning of the process.

1) High Maintenance

SVM is great when you want to get into the fine tuning aspect of Machine Learning.

A good side effect of being involved in optimization is that you learn and understand more about data and its details.

Since SVM is not an ideal algorithm for out-of-box usage it will allow and require you to twist its many parameters such as kernels which will force you to understand the data-set better.

2) Low Performance

SVM is great when you want to get into the fine tuning aspect of Machine Learning.

A good side effect of being involved in optimization is that you learn and understand more about data and its details.

Since SVM is not an ideal algorithm for out-of-box usage it will allow and require you to twist its many parameters such as kernels which will force you to understand the data-set better.

3) No Edge

SVM is great when you want to get into the fine tuning aspect of Machine Learning.

A good side effect of being involved in optimization is that you learn and understand more about data and its details.

Since SVM is not an ideal algorithm for out-of-box usage it will allow and require you to twist its many parameters such as kernels which will force you to understand the data-set better.

Many Options for Customization

Benefits of Kerneling

The weakest selling point of SVM is that it requires lots of fine tuning and adjustments and when not optimized correctly it doesn’t offer any superior benefits to some of the other supervised machine learning algorithms. 

In this article we will uncover some of the disadvantages (or cons) of Support Vector Machines and try to explain them. For the same reason of high optimization requirements, SVM is attractive to some people.

Like most complicated things SVMs can get you hooked once you master them. For advantages (or pros) of Support Vector Machines you can check out the article below:

4) Overfitting Risk

SVM is great when you want to get into the fine tuning aspect of Machine Learning.

A good side effect of being involved in optimization is that you learn and understand more about data and its details.

Since SVM is not an ideal algorithm for out-of-box usage it will allow and require you to twist its many parameters such as kernels which will force you to understand the data-set better.

5) No Probability

SVM is great when you want to get into the fine tuning aspect of Machine Learning.

A good side effect of being involved in optimization is that you learn and understand more about data and its details.

Since SVM is not an ideal algorithm for out-of-box usage it will allow and require you to twist its many parameters such as kernels which will force you to understand the data-set better.

6) Model Adjustments

SVM is great when you want to get into the fine tuning aspect of Machine Learning.

A good side effect of being involved in optimization is that you learn and understand more about data and its details.

Since SVM is not an ideal algorithm for out-of-box usage it will allow and require you to twist its many parameters such as kernels which will force you to understand the data-set better.

Many Options for Customization

Benefits of Kerneling

Alternatively if your data is dimensional and scaling is an important factor or if you need probability reports you can check out:

Summary

Support Vector Machines are not as popular as they used to be when the idea was fresh. This can be attributed to the strong competition. Simply, there are algorithms that can also accurately predict classification or regression problems with much less hassle if you will. This often leads to the debate that Support Vector Machines don’t have an edge in the race.

However, if you want to go the custom route (which has its own benefits) or your project involves kernelling which SVM natively supports, then Support Vector Machine is your algorithm. Besides it’s pretty versatile and can do classification, regression and even clustering. SVM offers an intellectual playground for the curious minds.

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