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Principles of Data Science

You're reading from   Principles of Data Science Understand, analyze, and predict data using Machine Learning concepts and tools

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789804546
Length 424 pages
Edition 2nd Edition
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Authors (3):
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Sunil Kakade Sunil Kakade
Author Profile Icon Sunil Kakade
Sunil Kakade
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
Marco Tibaldeschi Marco Tibaldeschi
Author Profile Icon Marco Tibaldeschi
Marco Tibaldeschi
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Table of Contents (17) Chapters Close

Preface 1. How to Sound Like a Data Scientist FREE CHAPTER 2. Types of Data 3. The Five Steps of Data Science 4. Basic Mathematics 5. Impossible or Improbable - A Gentle Introduction to Probability 6. Advanced Probability 7. Basic Statistics 8. Advanced Statistics 9. Communicating Data 10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials 11. Predictions Don't Grow on Trees - or Do They? 12. Beyond the Essentials 13. Case Studies 14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service Other Books You May Enjoy Index

The bias/variance trade-off

We have discussed the concept of bias and variance briefly in the previous chapters. When we are discussing these two concepts, we are generally speaking of supervised learning algorithms. We are specifically talking about deriving errors from our predictive models due to bias and variance.

Errors due to bias

When speaking of errors due to bias, we are speaking of the difference between the expected prediction of our model and the actual (correct) value, which we are trying to predict. Bias, in effect, measures how far, in general, our model's predictions are from the correct value.

Think about bias as simply being the difference between a predicted value and the actual value. For example, consider that our model, represented as F(x), predicts the value of 29, as follows:

Errors due to bias

Here, the value of 29 should have been predicted as 79:

Errors due to bias

If a machine learning model tends to be very accurate in its prediction (regression or classification), then it is considered a low bias...

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