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Variable preliminary filtering

 

There are often some variables in the data that are of poor quality or are not meaningful to the model. You can define some rules and delete them directly to reduce the amount of computation. Such as:

（1）Variables with high missing rate

（2）Unary variable

（3）Variable with too many categories

A

1

=file("D://house_prices_train.csv").import@qtc()

2

=A1.fname()

3

=A2.((y=~,A1.align@a([true,false],!eval(y))))

4

=A2.new(~:col,A3(#)(1).len():null_no,round(null_no/A1.len(),3):null_rate,A1.field(#).icount():icount,if(!sum(A1.field(#)),"string","num"):type)

5

=A4.select(null_rate<0.9 && icount>1 && !(type=="string" && icount>100)).(col).concat(",")

6

=A1.new(${A5})

A2 Get field names

A3 Divide each field into two groups according to whether is missing

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A4 Count the missing number , missing rate, number of distinct values, and data type of each field.

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A5 Delete variables with a miss rate greater than 0.9, unary variables, and variables with a class number greater than 100

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A6 Table after variable filtering


 
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