Applied Data Science Coding with Python: SVM Algorithm

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Applied Data Science Coding with Python: SVM Algorithm

The Support Vector Machine (SVM) algorithm is a method for classification and regression in machine learning. It is used to find the best boundary (or hyperplane) that separates different classes in the dataset with the greatest possible margin.

The SVM algorithm starts by mapping the input data to a higher dimensional space, where a boundary can be found to separate the classes. The boundary that maximizes the margin between the classes is chosen as the best boundary. Data points that are closest to the boundary are called support vectors, and they play a crucial role in determining the position of the boundary.

In order to use the SVM algorithm in Python, you need to have a dataset that includes both the input data and the labels or outcomes. You also need to decide on the type of SVM algorithm that will be used, there are different types of SVM algorithm such as Linear SVM, Nonlinear SVM, and others.

There are several libraries available in Python to implement the SVM algorithm, such as scikit-learn, NumPy, and Pandas. These libraries provide pre-built functions and methods to build, train and evaluate a SVM model.

It is important to note that SVM algorithm might be sensitive to the scale of the features, so it’s important to scale the features before using the algorithm. Also, SVM algorithm might not work well if the data is not linearly separable and the kernel should be chosen carefully.

In summary, The Support Vector Machine (SVM) algorithm is a method for classification and regression in machine learning. It finds the best boundary that separates different classes in the dataset with the greatest possible margin. There are several libraries available in Python to implement the SVM algorithm and it’s important to scale the features before using the algorithm and also to choose the kernel carefully if the data is not linearly separable.

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to apply SVM Algorithm.



Applied Data Science Coding with Python: SVM Algorithm

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