![]() It usually helps to visualize your data to see what you are working with. This section is really just to show what the images and labels look like. To see that there are 1797 images and 1797 labels in the dataset Showing the Images and the Labels (Digits Dataset) Now that you have the dataset loaded you can use the commands below # Print to show there are 1797 images (8 by 8 images for a dimensionality of 64) print(“Image Data Shape”, ) # Print to show there are 1797 labels (integers from 0–9) print("Label Data Shape", ) from sklearn.datasets import load_digits digits = load_digits() The code below will load the digits dataset. The digits dataset is one of datasets scikit-learn comes with that do not require the downloading of any file from some external website. Install Anaconda on Ubuntu (Linux): Link Logistic Regression on Digits Dataset Loading the Data (Digits Dataset) You can either download anaconda from the official site and install on your own or you can follow these anaconda installation tutorials below to set up anaconda on your operating system. 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. MNIST Logistic Regression (second part of tutorial code) Getting Started (Prerequisites) The code used in this tutorial is available belowĭigits Logistic Regression (first part of tutorial code) If you get lost, I recommend opening the video above in a separate tab. The second part of the tutorial goes over a more realistic dataset (MNIST dataset) to briefly show how changing a model’s default parameters can effect performance (both in timing and accuracy of the model). The first part of this tutorial post goes over a toy dataset (digits dataset) to show quickly illustrate scikit-learn’s 4 step modeling pattern and show the behavior of the logistic regression algorthm.
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