Automated Feature Engineering in SAS Model Studio on SAS Viya
Properly engineering and selecting your features is as important as choosing and tuning your models. Good feature engineering can vastly improve your model results. But who has time for this? Automated feature engineering and selection available with SAS Visual Data Mining and Machine Learning is your friend!
The Data Science Pilot Action Set has 3 actions to help you out:
- featureMachine
- generateShadowFeatures
- selectFeatures
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The Feature Machine Node The feature machine node in Model Studio transforms features to improve data quality and improve model accuracy. These transformations can fix:
- high cardinality
- low entropy
- high kurtosis
- missing values
- outliers
- low indices of qualitative variation
- high skewness
Cardinality, missingness, and skewness are selected by default. You can leave those selected or deselect them, and add any combination of the others.
featureMachine action The featureMachine action works by:
- Exploring the feature transformation and generation space
- Executing the potentially effective transformation and generation operators
And it is highly efficient because it does all this without generating temporary data tables. The featureMachine action composes a sequence of operators to construct the feature transformation and generation processes that it executes. The processes are as follows:
- Missing indicator
- Mode imputation and group rare
- Missing level and group rare
- Median imputation
- Mode imputation and label encoding
- Missing level and label encoding
- Yeo-Johnson transformation and median imputation
- Box-Cox transformation
- Quantile binning with missing bins
- Decision (classification) tree binning
- Regression tree binning
- MDLP binning
- Target encoding
- Date, time, datetime transformations
- Interaction features
Missing indicator generates binary missing indicator features from the input variables. It applies to variables whose missing rates:
- exceed the value of the missingIndicatorPercent parameter and
- are lower than the value of the missingPercent subparameter of the screenPolicy parameter
Mode imputation and group rare applies to nominal variables that have very low missing values, It imputes missing values with mode value. It also combines rare levels into a separate group called _OTHER_.
Missing level and label encoding applies to nominal variables whose missing rate makes them ineligible for mode imputation. It creates a new missing level and transforms the resulting variable by using the label encoding transformation. Label encoding assigns each label a unique integer based on alphabetical ordering.
Median imputation applies to all interval variables, except those that are converted to binary missing indicator variables.
Mode imputation and label encoding applies to nominal variables that have very low missing values. It imputes missing values using the mode. It transforms the resulting variable using the label encoding transformation, which assigns each label a unique integer based on alphabetical ordering.
Missing level and group rare applies to nominal variables whose missing rate makes them ineligible for mode imputation. It creates a new missing level and combines rare levels into a separate group called _OTHER_.
Yeo-Johnson transformation and median imputation is for interval variables with significant kurtosis.
It first transforms the variable using Yeo-Johnson transformation and then imputes using the median.
Box-Cox transformation is for interval variables with significant skewness.
It applies the Box-Cox transformation, then imputes missing values using the median.
Quantile binning with missing bins applies to interval variables with significant:
- skewness
- kurtosis, or
- outliers
Decision (classification) tree binning and Regression tree binning are used for both interval and nominal input variables. Like quantile binning, they are used to address variables with significant skewness, kurtosis, or outliers. They are commonly applied to high cardinality nominal variables. The featureMachine action uses classification decision tree for classification problems and uses regression tree for regression problems.
MDLP binning applies a binning algorithm based on the minimum description length principle (MDLP). MDLP is a top-down supervised discretization technique. The conditions for applying it are similar to those for decision tree binning.
Target encoding is for variables with significant distinct counts. For regression problems, target encoding operators include:
- mean
- minimum
- maximum
For classification problems, target encoding operators include:
- frequency ratio
- event probability
- weight of evidence
Label count and input count encodings (and their log transformations) are applicable to both regression and classification problems. Date, time, datetime transformations are specific to date, time, and datetime variables.
Operators that extract a specific piece of information from these variables are applied. The operators include year, month, day, day of the week, day of the month, leap year (binary indicator), weekend (binary indicator), etc.
Interaction features are not generated by default. If the interaction subparameter of the transformationPolicy parameter is set to True, interaction features are generated from variable pairs with strong interactions. The operators include various target encoding operators, e.g.,
- mean
- weight of evidence
- event probability
- decision tree and regression tree binning
These operators act on the binned crossproduct (frequency table) of the interacting variables.
generateShadowFeatures Action
generateShadowFeatures uses shadow features to select relevant features. A shadow feature contains values of the original feature chosen at random. It relies on inverse sampling:
• Using empirical cumulative distribution for continuous variables
• Using empirical frequency distribution for nominal variables
This is accomplished in a single pass through the data. The nProbes parameter lets you specify the number of shadow features to generate per variable.
selectFeatures Action
The selectFeatures Action filters out features using a user-specified criterion (statistic). All the correlation statistics available in the exploreCorrelation action are also available here. If user picks a filter criterion that is not applicable to any of the inputs, then the default statistic—mutual information—is used.
Conclusion
So what is stopping you? Jump in and take advantage of these automated SAS tools and improve your feature engineering!
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