Preprocess: LDA and Kernel PCA in Python

Principal component analysis (PCA) is an unsupervised linear transformation technique that is widely used across different fields, most prominently for dimensionality reduction. We talked about it here: https://charleshsliao.wordpress.com/2017/05/28/preprocess-pca-application-in-python/ We use the data from sklearn library, and the IDE is Python3. Most of the code comes from Sebastian Raschka’s book: https://www.goodreads.com/book/show/25545994-python-machine-learning?ac=1&from_search=true ###1. import the data ###pls […]

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Pipeline Steps in Python

We use the data from sklearn library(need to download face datasets separately), and the IDE is sublime text3. Most of the code comes from the book: https://www.goodreads.com/book/show/32439431-introduction-to-machine-learning-with-python?from_search=true ###################always keep the below code##################### import os import sys sys.path.append(‘//anaconda/lib/python3.6/site-packages’) ###################always keep the above code##################### ###we can use the Pipeline to express the work-flow for training an SVM […]

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Preprocess: t-SNE in Python

We use the data from sklearn library, and the IDE is sublime text3. Most of the code comes from the book: https://www.goodreads.com/book/show/32439431-introduction-to-machine-learning-with-python?from_search=true ###There is a class of algorithms for visualization called manifold learning algorithms ###which allows for much more complex mappings, and often provides better visualizations compared with PCA. ###A particular useful one is the […]

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Preprocess: PCA Application in Python

We use the data from sklearn library, and the IDE is sublime text3. Most of the code comes from the book: https://www.goodreads.com/book/show/32439431-introduction-to-machine-learning-with-python?from_search=true ###sometimes we might face the situation that the features or vars in the data are not separate from each other ###We can always observe that data before we can even preprocess it with […]

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