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Scale Data for Machine Learning

Scaling (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. Machine Learning for Engineers: https://apmonitor.com/pds Data Scaling: https://apmonitor.com/pds/index.php/M...