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The Data Science Workshop

You're reading from   The Data Science Workshop A New, Interactive Approach to Learning Data Science

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Product type Paperback
Published in Jan 2020
Publisher
ISBN-13 9781838981266
Length 818 pages
Edition 1st Edition
Languages
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Authors (5):
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Thomas Joseph Thomas Joseph
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Thomas Joseph
Andrew Worsley Andrew Worsley
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Andrew Worsley
Robert Thas John Robert Thas John
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Robert Thas John
Anthony So Anthony So
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Anthony So
Dr. Samuel Asare Dr. Samuel Asare
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Dr. Samuel Asare
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Toc

Table of Contents (18) Chapters Close

Preface 1. Introduction to Data Science in Python 2. Regression FREE CHAPTER 3. Binary Classification 4. Multiclass Classification with RandomForest 5. Performing Your First Cluster Analysis 6. How to Assess Performance 7. The Generalization of Machine Learning Models 8. Hyperparameter Tuning 9. Interpreting a Machine Learning Model 10. Analyzing a Dataset 11. Data Preparation 12. Feature Engineering 13. Imbalanced Datasets 14. Dimensionality Reduction 15. Ensemble Learning 16. Machine Learning Pipelines 17. Automated Feature Engineering

Strategies for Dealing with Imbalanced Datasets

Now that we have identified the challenges of imbalanced datasets, let's look at strategies for combatting imbalanced datasets:

Figure 13.7: Strategies for dealing with imbalanced datasets

Collecting More Data

Having encountered an imbalanced dataset, one of the first questions you need to ask is whether it is possible to get more data. This might appear naïve, but collecting more data, especially from the minority class, and then balancing the dataset should be the first strategy for addressing the class imbalance.

Resampling Data

In many circumstances, collecting more data, especially from minority classes, can be challenging as data points for the minority class will be very minimal. In such circumstances, we need to adopt different strategies to work with our constraints and still strive to balance our dataset. One effective strategy is to resample our dataset to make the dataset...

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