Why use Pipeline?
A machine learning pipeline is like an assembly line where many processes are are connected sequentially, such as preparing data, training the data, etc. So, it becomes easier to work with from start to end.
Even we can do our all our task from training to testing without the pipeline, so what is the real benefit of using pipeline? There are many reasons to use pipeline, let’s discuss few of them:
- When using pipeline, same preprocessing steps can be applied to both training and testing data without writing different code for both of them.
- It encapsulates the entire work into single object.
- It is most useful when label encoding has to be done for both training and testing.
- It reduces the code length as when we want to train for multiple times we can use that pipeline.
PCA and SVM Pipeline in Python
Principal Component Analysis (PCA) and Support Vector Machines (SVM) are powerful techniques used in machine learning for dimensionality reduction and classification, respectively. Combining them into a pipeline can enhance the performance of the overall system, especially when dealing with high-dimensional data. The aim of the article is demonstrate how we can utilize PCA and SVM in single pipeline in Python.