January 2018
Beginner to intermediate
316 pages
7h 14m
English
Content preview from Feature Engineering Made Easy
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MNIST dataset
The MNIST dataset consists of 6,000 images of handwritten digits between zero and nine and a ground-truth label to learn from. It is not unlike most of the other datasets that we have been working with in that we are attempting to fit a machine learning model to classify a response variable given a set of data points. The main difference here is that we are working with very low-level features as opposed to more interpretable features. Each data point will consist of 784 features (pixel values in a grey-scale image).
- Let's begin with our imports:
# import numpy and matplotlibimport numpy as npimport matplotlib.pyplot as plt%matplotlib inlinefrom sklearn import linear_model, datasets, metrics# scikit-learn implementation of ...
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