1/28/2024 0 Comments Reduce dataframe columns pca![]() ![]() I recommend having anaconda installed (either Python 2 or 3 works well for this tutorial) so you won’t have any issue importing libraries. If you already have anaconda installed, skip to the next section. PCA to Speed-up Machine Learning Algorithms Getting Started (Prerequisites) The code used in this tutorial is available below With that, let’s get started! If you get lost, I recommend opening the video below in a separate tab. The second part uses PCA to speed up a machine learning algorithm (logistic regression) on the MNIST dataset. To understand the value of using PCA for data visualization, the first part of this tutorial post goes over a basic visualization of the IRIS dataset after applying PCA. Another common application of PCA is for data visualization. This is probably the most common application of PCA. ![]() If your learning algorithm is too slow because the input dimension is too high, then using PCA to speed it up can be a reasonable choice. A more common way of speeding up a machine learning algorithm is by using Principal Component Analysis (PCA). ![]() One of the things learned was that you can speed up the fitting of a machine learning algorithm by changing the optimization algorithm. My last tutorial went over Logistic Regression using Python. ![]()
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