![]() ![]() The limit for distinct values is set to 100 by default. ![]() In this scenario the high-cardinality field is removed by the fit command, but the field that contains the generated calculations remains. You must generate these calculations in your search before the fit command. For example, by using SPL commands such as streamstats or eventstats, you can calculate the number of times an IP address occurs in your search results. Had the search results included more than 100 distinct Internet Protocol (IP) addresses in field_E it would qualify as high-cardinality.Īn alternative to discarding fields is to use the values to generate a usable feature set. In this example, none of the fields have a non-numeric field with more than 100 distinct values, so no action is taken. ![]() In MLTK, IP numbers are interpreted as non-numeric or string values. A high-cardinality field can lead to an explosion in feature space very quickly. In machine learning, many algorithms do not perform well with high-cardinality fields, because every unique, non-numeric entry in a field becomes an independent feature. The fit command discards non-numeric fields if the fields have more than 100 distinct values. | fillnull field_C | fit LogisticRegression field_A from field_* Discard non-numeric fields with more than (>) 100 distinct values. You must specify the fillnull command before the fit command, as shown in the following search example: For example, to replace the null values with 0 in the results for field_C, use the SPL fillnull command. ![]() If you do not want null fields to be removed from the search results you must change your search. The column lableled field_C is highlighted for removal because there are no values in this field. In this example, the fit command looks for incidents of fraud within the dataset. The following example shows a simplified visual representation of the search results. The fit command discards fields that contain no values. The following actions all take place on the search results copy.ĭiscard any fields that are null throughout all the events The data must be properly prepared to be suitable for machine learning and running though the selected algorithm. Transform search results using data preparation actions The originally ingested data is not changed. When you run a search, the fit command pulls the search results into memory, creates a copy of the search results, and parses the search results into Pandas DataFrame format.
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