Mastering Feature Encoding for Machine Learning: A Comprehensive Guide
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In this riveting video from NeuralNine, the team delves into the thrilling world of feature encoding for machine learning. They kick things off by emphasizing the crucial need to transform non-numerical features into numerical values for the ultimate showdown in model training. Label encoding is showcased as a method that boldly maps values to numbers without any specific order, setting the stage for a wild ride in the data transformation arena.
Next up, the team shifts gears to explore the adrenaline-pumping world of ordinal encoding, where values are assigned a specific order to rev up the engine of feature representation. The audience is then taken on a heart-pounding journey through the treacherous terrain of one-hot encoding, where categorical features are transformed into binary features, ready to conquer the challenges of decision trees and beyond.
As the excitement reaches a fever pitch, the team introduces the audience to the high-octane world of binary encoding, a method tailored for high cardinality features that represent values as binary numbers, unleashing a new level of power and efficiency in data representation. The audience is then thrown into a thrilling spin with frequency encoding, a daring move that replaces values with their frequency, offering a unique twist in feature encoding strategies.
And just when you thought the excitement couldn't get any higher, the team unveils the exhilarating concept of target encoding, where the mean target value for a feature value takes center stage, promising a heart-stopping ride through the unpredictable landscape of data manipulation. The grand finale of this adrenaline-fueled journey culminates in the unveiling of embeddings, a cutting-edge technology that transforms values into high-dimensional vector spaces, setting the stage for a future where machine learning reaches new heights of performance and innovation.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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Transformation of features into usable numbers for better model performance
101 series breakdown with clear bullet points
Request for demonstration of feature transformation in a sklearn pipeline
Reminder to only do frequency and target encoding on training sets
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