Normalization and Data Scaling in R
Understand normalization and data scaling techniques in R to standardize features for better machine learning outcomes. Learn to apply min-max scaling and z-score normalization on datasets, reducing bias from differing data ranges and improving computational efficiency.
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Data normalization
Data normalization and scaling are techniques used to standardize the range and distribution of the variables in a dataset. This is important because many machine learning algorithms use distance-based calculations, such as Euclidean distance, to compare samples and make predictions. If the features in a dataset have different scales and ranges, then some features may dominate others, causing the model to be biased toward those features.
Normalization and scaling can help avoid this by transforming the values so that they have the same scale and range.