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[DOCS] Add custom feature processor example (#64681) (#64737)

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docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc
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@ -627,6 +627,95 @@ PUT _ml/data_frame/analytics/student_performance_mathematics_0.3
<1> The percentage of the data set that is used for training the model.
<2> The seed that is used to randomly pick which data is used for training.
The following example uses custom feature processors to transform the
categorical values for `DestWeather` into numerical values using one-hot,
target-mean, and frequency encoding techniques:
[source,console]
--------------------------------------------------
PUT _ml/data_frame/analytics/flight_prices
{
"source": {
"index": [
"kibana_sample_data_flights"
]
},
"dest": {
"index": "kibana_sample_flight_prices"
},
"analysis": {
"regression": {
"dependent_variable": "AvgTicketPrice",
"num_top_feature_importance_values": 2,
"feature_processors": [
{
"frequency_encoding": {
"field": "DestWeather",
"feature_name": "DestWeather_frequency",
"frequency_map": {
"Rain": 0.14604811155570188,
"Heavy Fog": 0.14604811155570188,
"Thunder & Lightning": 0.14604811155570188,
"Cloudy": 0.14604811155570188,
"Damaging Wind": 0.14604811155570188,
"Hail": 0.14604811155570188,
"Sunny": 0.14604811155570188,
"Clear": 0.14604811155570188
}
}
},
{
"target_mean_encoding": {
"field": "DestWeather",
"feature_name": "DestWeather_targetmean",
"target_map": {
"Rain": 626.5588814585794,
"Heavy Fog": 626.5588814585794,
"Thunder & Lightning": 626.5588814585794,
"Hail": 626.5588814585794,
"Damaging Wind": 626.5588814585794,
"Cloudy": 626.5588814585794,
"Clear": 626.5588814585794,
"Sunny": 626.5588814585794
},
"default_value": 624.0249512020454
}
},
{
"one_hot_encoding": {
"field": "DestWeather",
"hot_map": {
"Rain": "DestWeather_Rain",
"Heavy Fog": "DestWeather_Heavy Fog",
"Thunder & Lightning": "DestWeather_Thunder & Lightning",
"Cloudy": "DestWeather_Cloudy",
"Damaging Wind": "DestWeather_Damaging Wind",
"Hail": "DestWeather_Hail",
"Clear": "DestWeather_Clear",
"Sunny": "DestWeather_Sunny"
}
}
}
]
}
},
"analyzed_fields": {
"includes": [
"AvgTicketPrice",
"Cancelled",
"DestWeather",
"FlightDelayMin",
"DistanceMiles"
]
},
"model_memory_limit": "30mb"
}
--------------------------------------------------
// TEST[skip:TBD]
NOTE: These custom feature processors are optional; automatic
{ml-docs}/ml-feature-encoding.html[feature encoding] still occurs for all
categorical features.
[[ml-put-dfanalytics-example-c]]
=== {classification-cap} example