Prepare Data for Machine Processing
Depending on your downstream system, you may need to convert your data into numeric values of the expected form or to standardize the distribution of numeric values. This section summarizes some common statistical transformations that can be applied to columnar data to prepare it for use in downstream analytic systems.
Scaling
You can scale the values within a column using either of the following techniques.
Scale to zero mean and unit variance
Zero mean and unit variance scaling renders the values in the set to fit a normal distribution with a mean of 0
and a variance of 1
. This technique is a common standard for normalizing values into a normal distribution for statistical purposes.
In the following example, the values in the POS_Sales
column have been normalized to average 0
, variance 1
.
Remove mean: When selected, the existing mean (average) of the values is used as the center of the distribution curve.
Note
Recentering sparse data by removing the mean may remove sparseness.
Scale to unit variance: When selected, the range of values are scaled such that their variance is
1
. When deselected, the existing variance is maintained.Note
Scaling to unit variance may not work well for managing outliers. Some additional techniques for managing outliers are outlined below.
Transformation Name 


Parameter: Column  POS_Sales 
Parameter: Scaling method  Scale to zero mean and unit variance 
Parameter: Remove mean  false 
Parameter: Scale to unit variance  true 
Parameter: Output options  Create new column 
Parameter: New column name  scale_POS_Sales 
Scale to minmax range
You can scale column values fitting between a specified minimum and maximum value. This technique is useful for distributions with very small standard deviation values and for preserving 0 values in sparse data.
The following example scales the TestScores
column to a range of 0
and 1
, inclusive.
Transformation Name 


Parameter: Column  TestScores 
Parameter: Scaling method  Scale to a given minmax range 
Parameter: Minimum  0 
Parameter: Maximum  1 
Parameter: Output options  Replace current column 
Outliers
You can use several techniques for identifying statistical outliers in your dataset and managing them as needed.
Identify outliers
Suppose you need to remove the outliers from a column. Assuming a normal bell distribution of values, you can use the following formula to calculate the number of standard deviations a column value is from the column mean (average). In this case, the source column is POS_Sales
.
Transformation Name 


Parameter: Formula type  Multiple row formula 
Parameter: Formula  (ABS(POS_Sales  AVERAGE(POS_Sales))) / STDEV(POS_Sales) 
Parameter: New column name  stdevs_POS_Sales 
Remove outliers
The new stdevs_POS_Sales
column now contains the number of standard deviations from the mean for the corresponding value in POS_Sales
. You can use the following transformation to remove the rows that contain outlier values for this column.
Tip
An easier way to select these outlier values is to select the range of values in the stdevs_POS_Sales
column histogram. Then, select the suggestion to delete these rows. You may want to edit the actual formula before you add it to your recipe.
In the following transformation, all rows that contain a value in POS_Sales
that is greater than four standard deviations from the mean are deleted:
Transformation Name 


Parameter: Condition  Custom formula 
Parameter: Type of formula  Custom single 
Parameter: Condition  4 <= stdevs_POS_Sales 
Parameter: Action  Delete matching rows 
Change outliers to mean values
You can also remove the effects of outliers be setting their value to the mean (average), which preserves the data in other columns in the row.
Transformation Name 


Parameter: Columns  POS_Sales 
Parameter: Formula  IF(stdevs_POS_Sales > 4, AVERAGE(POS_Sales), POS_Sales) 
Binning
You can modify your data to fit into bins of equal or custom size. For example, the lowest values in your range would be marked in the 0
bin, with larger values being marked with larger bin numbers.
Bins of equal size
You can bin numeric values into bins of equal size. Suppose your column contains numeric values 01000
. You can bin values into equal ranges of 100
by creating 10
bins.
Transformation Name 


Parameter: Column  MilleBornes 
Parameter: Select Option  Equal Sized Bins 
Parameter: Number of Bins  10 
Parameter: New column name  MilleBornesRating 
Bins of custom size
You can also create custom bins. In the following example, the TestScores
column is binned into the following bins. In a later step, these bins are mapped to grades:
Bins  Bin Range  Bin Number  Grade 

59  059  0  F 
69  6069  1  D 
79  7079  2  C 
89  8089  3  B 
90+  4  A  
(no value)  I 
First, you bin values into the bin numbers listed above:
Transformation Name 


Parameter: Column  TestScores 
Parameter: Select option  Custom bin size 
Parameter: Bins  59,69,79,89 
Parameter: New column name  Grades 
You can then use the following transformation to assign letters in the Grades
column:
Transformation Name 


Parameter: Condition type  Case on single column 
Parameter: Column to evaluate  Grades 
Parameter: Case  0  'F' 
Parameter: Case  1  'D' 
Parameter: Case  2  'C' 
Parameter: Case  3  'B' 
Parameter: Case  4  'A' 
Parameter: Default value  'I' 
Parameter: New column name  Grades_letters 
OneHot Encoding
Onehot encoding refers to distributing the listed values in a column into individual columns. Within each row of each individual column is a 0
or a 1
, depending on whether the value represented by the column appears in the corresponding source column. The source column is untouched. This method of encoding allows for easier consumption of data in target systems.
Tip
This transformation is particularly useful for columns containing a limited set of enumerated values.
In the following example, the values in the BrandName
column are distributed into separate columns of binary values, with a maximum limit of 50
new columns.
Note
Be careful applying this to a column containing a wide variety of values, such as Decimal values. Your dataset can expand significantly in size. Use the max columns setting to constrain the upper limit on dataset expansion.
Transformation Name 


Parameter: Column  BrandName 
Parameter: Max number of columns to create  50 
Tip
If needed, you can rename the columns to prepend the names with a reference to the source column.