Support Vector Machine(Machine Learning)

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Support Vector Machine(SVM) are Supervised Learning models which can be used for both classification or regression, but it’s mainly used for Classification problems.

What it does?

Firstly, each data item is plotted as a point in the space & the value of each point corresponds to a particular coordinate. Then the classification is done by a separator called ‘Hyperplane’ & it is put in a place such that the ‘Margin’ remains maximum.

Applications

• Face Detection.
• Classification of images.
• Bioinformatics.
• Handwriting Detection.

and many more…

Using SVM

Way 1- Directly importing from scikit learn library in Python.

Input- from sklearn import svm

This imports the SVM.

Way 2- Implementing SVM Model from Scratch.

Limitations

• Not suitable for Large Datasets.
• When the number of features for each data point exceeds the number of training data samples, the SVM underperforms.
• It classifies through geometry whereas a lot of classification problems probability gives better results.

This was all about Support Vector Machine.

Happy Learning Folks!

You can also visit my previous blogs by clicking on their name below-

• Google Colab
• Pandas
• NumPy
• Matplotlib
• OpenCV
• Data Preprocessing
• Linear Regression
• Decision Tree & Random Forest
• K-Nearest Neighbors
• Logistic Regression

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