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Feature Engineering für Machine Learning in Python

Robert O'Callaghan

Director of Data Science, Ordergroove

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Feature Engineering für Machine Learning in Python

Quantilbasierte Erkennung

Feature Engineering für Machine Learning in Python

Quartile in Python

q_cutoff = df['col_name'].quantile(0.95)

mask = df['col_name'] < q_cutoff

trimmed_df = df[mask]
Feature Engineering für Machine Learning in Python

Standardabweichungsbasierte Erkennung

Feature Engineering für Machine Learning in Python

Erkennung per Standardabweichung in Python

mean = df['col_name'].mean()
std = df['col_name'].std()

cut_off = std * 3

lower, upper = mean - cut_off, mean + cut_off

new_df = df[(df['col_name'] < upper) & 
                 (df['col_name'] > lower)]
Feature Engineering für Machine Learning in Python

Lass uns üben!

Feature Engineering für Machine Learning in Python

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