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<<< SVM Algorithm

  1. Overview
  2. Classification
  3. Regression
  4. Advantages
  5. Disadvantages
  6. Support Vector Machine Complexity
  7. Tuning Support Vector Machine
  8. Who Invented SVM?
  9. Support Vector Machine Example 1

Support Vector Machine Advantages

Many Options for Customization

1- Benefits of Kerneling

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1) Advanced Settings

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) Linear SVM

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) Fast to Predict

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

4- Benefits of Kerneling

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.

SVM Handles Data at RAM Level

5- Fast Predictions

Once the training phase is finished you will realize that predictions are very fast. This is thanks to SVM’s training method that takes a bit longer but does most of the job.

Precision Helps the Outcomes

6- Accurate Results

With the SVMs you see the results of optimizations tremendously and predictions with very high accuracy can be achieved this way. This also supports learning more about the data.

Summary

Advantages

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