Jan 2018, in Machine Learning, Workshop
Dimensionality Reduction - Principal Component Analysis Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. Sometimes data has redundant dimensions. For example, when predicting weight from height data you would expect that information about their eye colour provides no predictive power. In this simple case we can simply remove that feature from the data. With more complex data it is usual to have combinations of features that provide predictive power.