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\nImbalanced Data Classification\n

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\n\nPublisher:\n\nAPMonitor\n\n\n
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\nImbalanced data is a disproportionate number of data points with discrete labels and can be a big challenge to develop an accurate classifier. A classifier attempts to find the data boundary where one class ends and the other begins....\nImbalanced data is a disproportionate number of data points with discrete labels and can be a big challenge to develop an accurate classifier. A classifier attempts to find the data boundary where one class ends and the other begins. [[Main/ClassificationOverview|Classification]] is used to create these boundaries when the desired output (label) is discrete such as 0/1, Yes/No, 1,2,3,4,5, or Normal/Abnormal. Regression is used when the desired output (label) is continuous.

Imbalanced data can give biased predictions for classification when there is a minority class. This tutorial demonstrates how to deal with imbalanced data.\n
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Resource Details

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Curator Rating
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Educator Rating
Not yet Rated
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Media Length
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37:57
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Grade
10th - Higher Ed
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Subjects
STEM
3 more...
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Media Type
Instructional Videos
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Source:
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\nAPMonitor.com\n\n
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Date
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2021
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Language
English
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Audiences
For Teacher Use
2 more...
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Usage Permissions
Fine Print: Educational Use
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Concepts

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sampling, bias
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Additional Tags

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apmonitor, imbalanced, imbalance, equal, minority, cleanse, adjust, data set, logistic regression, minority group, minority set, random state, confusion matrix, imb learn, fewer samples, mesh grid, x2 grid, x1 grid, model weighted, blue dots, majority set, orange dots, sampling strategy, make blobs, imbalanced data, random, learn, model, samples, grid, feature, strategy, results, blue, majority
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Classroom Considerations

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\n\" data-title=\"Get Full Access\" data-container=\"body\" rel=\"popover\" tabindex=\"0\" aria-label=\"Play video: Imbalanced Data Classification\" href=\"/subscription/new\">\"Imbalanced\n\n
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