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scikit-learn Cookbook - Second Edition

You're reading from  scikit-learn Cookbook - Second Edition

Product type Book
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286382
Pages 374 pages
Edition 2nd Edition
Languages
Author (1):
Trent Hauck Trent Hauck
Profile icon Trent Hauck

Table of Contents (13) Chapters

Preface 1. High-Performance Machine Learning – NumPy 2. Pre-Model Workflow and Pre-Processing 3. Dimensionality Reduction 4. Linear Models with scikit-learn 5. Linear Models – Logistic Regression 6. Building Models with Distance Metrics 7. Cross-Validation and Post-Model Workflow 8. Support Vector Machines 9. Tree Algorithms and Ensembles 10. Text and Multiclass Classification with scikit-learn 11. Neural Networks 12. Create a Simple Estimator

Perceptron classifier

With scikit-learn, you can explore the perceptron classifier and relate it to other classification procedures within scikit-learn. Additionally, perceptrons are the building blocks of neural networks, which are a very prominent part of machine learning, particularly computer vision.

Getting ready

Let's get started. The process is as follows:

  1. Load the UCI diabetes classification dataset.
  2. Split the dataset into training and test sets.
  3. Import a perceptron.
  4. Instantiate the perceptron.
  5. Then train the perceptron.
  6. Try the perceptron on the testing set or preferably compute cross_val_score.

Load the UCI diabetes dataset:

import numpy as np
import pandas as pd

data_web_address = "https://archive.ics...
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