Let’s try to predict whether a flight will be delayed or not by using the sample flight data. We want to be able to use information such as weather conditions, carrier, flight distance, origin, or destination to predict flight delays. There are only two possible outcome values: the flight is either delayed or not, therefore we use binary classification to make the prediction.

We have chosen this dataset as an example because it is easily accessible for Kibana users and the use case is relevant. However, the data has been manually created and contains some inconsistencies. For example, a flight can be both delayed and canceled. Please remember that the quality of your input data will affect the quality of results.

Each document in the dataset contains details for a single flight, so this data is ready for analysis as it is already in a two-dimensional entity-based data structure (data frame). In general, you often need to transform the data into an entity-centric index before you analyze the data.

This is an example source document from the dataset:

{
  "_index": "kibana_sample_data_flights",
  "_type": "_doc",
  "_id": "S-JS1W0BJ7wufFIaPAHe",
  "_version": 1,
  "_seq_no": 3356,
  "_primary_term": 1,
  "found": true,
  "_source": {
    "FlightNum": "N32FE9T",
    "DestCountry": "JP",
    "OriginWeather": "Thunder & Lightning",
    "OriginCityName": "Adelaide",
    "AvgTicketPrice": 499.08518599798685,
    "DistanceMiles": 4802.864932998549,
    "FlightDelay": false,
    "DestWeather": "Sunny",
    "Dest": "Chubu Centrair International Airport",
    "FlightDelayType": "No Delay",
    "OriginCountry": "AU",
    "dayOfWeek": 3,
    "DistanceKilometers": 7729.461862731618,
    "timestamp": "2019-10-17T11:12:29",
    "DestLocation": {
      "lat": "34.85839844",
      "lon": "136.8049927"
    },
    "DestAirportID": "NGO",
    "Carrier": "ES-Air",
    "Cancelled": false,
    "FlightTimeMin": 454.6742272195069,
    "Origin": "Adelaide International Airport",
    "OriginLocation": {
      "lat": "-34.945",
      "lon": "138.531006"
    },
    "DestRegion": "SE-BD",
    "OriginAirportID": "ADL",
    "OriginRegion": "SE-BD",
    "DestCityName": "Tokoname",
    "FlightTimeHour": 7.577903786991782,
    "FlightDelayMin": 0
  }
}

Notice that each document contains a FlightDelay field with a boolean value. Classification is a supervised machine learning analysis and therefore needs to train on data that contains the ground truth, known as the dependent_variable. In this example, the ground truth is available in each document as the actual value of FlightDelay. In order to be analyzed, a document must contain at least one field with a supported data type (numeric, boolean, text, keyword or ip) and must not contain arrays with more than one item.

If your source data consists of some documents that contain a dependent variable and some that do not, the model is trained on the subset of documents that contain ground truth. By default, all of that subset of documents is used for training. However, you can choose to specify a percentage of the documents as your training data. Predictions are made against all of the data. The current implementation of classification analysis supports a single batch analysis for both training and predictions.

GET df-flight-delayed/_search

          ...
          "FlightDelay" : false, 
          ...
          "ml" : {
            "top_classes" : [ 
              {
                "class_probability" : 0.939335365058496,
                "class_name" : "false"
              },
              {
                "class_probability" : 0.06066463494150393,
                "class_name" : "true"
              }
            ],
            "FlightDelay_prediction" : "false", 
            "is_training" : false 
          }

POST _ml/data_frame/_evaluate
{
 "index": "df-flight-delayed",  
   "query": {
    "term": {
      "ml.is_training": {
        "value": true  
      }
    }
  },
 "evaluation": {
   "classification": {
     "actual_field": "FlightDelay",  
     "predicted_field": "ml.FlightDelay_prediction",  
     "metrics": {
       "multiclass_confusion_matrix" : {}
     }
   }
 }
}

POST _ml/data_frame/_evaluate
{
 "index": "df-flight-delayed",
   "query": {
    "term": {
      "ml.is_training": {
        "value": false  
      }
    }
  },
 "evaluation": {
   "classification": {
     "actual_field": "FlightDelay",
     "predicted_field": "ml.FlightDelay_prediction",
     "metrics": {
       "multiclass_confusion_matrix" : {}
     }
   }
 }
}

{
  "classification" : {
    "multiclass_confusion_matrix" : {
      "confusion_matrix" : [
        {
          "actual_class" : "false", 
          "actual_class_doc_count" : 8778, 
          "predicted_classes" : [
            {
              "predicted_class" : "false", 
              "count" : 7509 
            },
            {
              "predicted_class" : "true",
              "count" : 1269
            }
          ],
          "other_predicted_class_doc_count" : 0
        },
        {
          "actual_class" : "true",
          "actual_class_doc_count" : 2939,
          "predicted_classes" : [
            {
              "predicted_class" : "false",
              "count" : 1213
            },
            {
              "predicted_class" : "true",
              "count" : 1726
            }
          ],
          "other_predicted_class_doc_count" : 0
        }
      ],
      "other_actual_class_count" : 0
    }
  }
}