February 2018
Intermediate to advanced
450 pages
11h 27m
English
Content preview from Deep Learning By Example
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Binning
This kind of quantitative transformation is used to create quantiles. In this case, the quantitative feature values will be the transformed ordered variable. This approach is not a good choice for linear regression, but it might work well for learning algorithms that respond effectively when using ordered/categorical variables.
The following code applies this kind of transformation to the Fare feature:
# Binarizing the features by binning them into quantilesdf_titanic_data['Fare_bin'] = pd.qcut(df_titanic_data['Fare'], 4)if keep_binary: df_titanic_data = pd.concat( [df_titanic_data, pd.get_dummies(df_titanic_data['Fare_bin']).rename(columns=lambda x: 'Fare_' + str(x))], axis=1)
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