Kibana version:
STACK_BUILD=2227
STACK_VERSION=7.7.0

Elasticsearch version:
STACK_BUILD=2227
STACK_VERSION=7.7.0

Server OS version:
N/A
Browser version:
Chrome
Browser OS version:
Mac OS X

Steps to reproduce:

1. Restore the seeds dataset and run the multiclass ML job linked in the Configuration section below
2. After job is complete (should be fast since this dataset only has 210 datapoints), click on "View" and look at the data in the table.
3. You will see that most of the entries in the data table are rounded to three decimal points, while some appear to display a lot more decimal points. This increased precision does not match what you would see if you looked at the data in Discover.

There are two issues here:

1. Inconsistent rounding among data points in the DF Analytics results table (see screenshot above)
2. Introducing more floating points than the source data

Source Data in the Discover tab

Same data point in the ML DF Analytics Results UI

ML Job Configuration

PUT _ml/data_frame/analytics/seeds
{
  "source": {
    "index": "seeds"
  },
  "dest": {
    "index":"seeds_results"
    
  },
  "model_memory_limit": "2gb",
  "analysis": 
    {
      "classification": {
        "num_top_classes" : 2,
        "dependent_variable": "seed_class",
        "training_percent": 80
      }
    }
}