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\nScale Data for Machine Learning\n

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\n\nPublisher:\n\nAPMonitor\n\n\n
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\nScaling (inputs and outputs) can improve the training process for machine learning. A common scaling technique is to divide by the standard deviation and shift the mean to 0. Another common scaling approach is to adjust all of the data...\nScaling (inputs and outputs) can improve the training process for machine learning. A common scaling technique is to divide by the standard deviation and shift the mean to 0. Another common scaling approach is to adjust all of the data to a range of 0 to 1 or -1 to 1. Each data column is scaled individually.

0:00 Overview
0:43 Equations
2:35 Scale 1D
4:37 Import Data
6:31 Split Data
7:22 Sklearn Standard Scaler
8:22 Scaling Factors
9:44 Transform Test Data
11:00 Numpy Array to DataFrame
12:31 Minmax Scaler
15:20 Inverse Transform
16:07 TCLab Histogram
18:16 Scale Data
20:42 Train Neural Network
27:43 Unscaled Neural Network
31:25 Summary

There are different methods for scaling that are important based on the presence of outliers or statistical properties of the data. Two primary methods for scaling are a standard scaler (scale by the standard deviation) and a min-max (e.g. 0-1) scaler. For classifiers and regressor such as neural networks, most of the data should be between 0 and 1 or -1 and 1\n
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Resource Details

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Educator Rating
Not yet Rated
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Media Length
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32:46
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Grade
10th - Higher Ed
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Subjects
Social Studies & History
5 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
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Usage Permissions
Fine Print: Educational Use
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Concepts

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standard deviation, values, scale
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Additional Tags

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apmonitor, minmax, normalize, preprocess, pipeline, preprocessing, scale data, hyperbolic tangent, max iterations, machine learning, neural network, standard scaler, read csv, preprocessing package, transition point, numpy array, fit transform, inverse transform, data frame, data engineering, set index, perfect prediction, figure size, test set, scatter plot, scaling values, bfgs solver, unscaled data, training data, test results, scaled, scaler, original, unscaled
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Classroom Considerations

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Best For: Explaining a topic
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\n\" data-title=\"Get Full Access\" data-container=\"body\" rel=\"popover\" tabindex=\"0\" aria-label=\"Play video: Scale Data for Machine Learning\" href=\"/subscription/new\">\"Scale\n\n
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