MetricType enum
Metrics for measuring the quality of the prediction.
Constructors
 MetricType()

const
Values
 mape → const MetricType

Mean percentage absolute error (MAPE), a regression metric. The formula is:
where
y
 original value,y
with hat  predicted oneThe less the score produced by the metric, the better the prediction's quality is. Can lead to error if there are zero values among the original values. Normally, the metric produces scores within the range
0, 1
(both included), but extremely high predicted values (>> original values) can produce scores which are greater than 1.  rmse → const MetricType

Root mean squared error (RMSE), a regression metric. The formula is
where
y
is an original value,y
with hat  predicted oneThe less the score produced by the metric, the better the prediction's quality is. The metric produces scores within the range
0, +Infinity
 rss → const MetricType

Residual sum of squares (RSS), a regression metric. The formula is
where
n
is a total amount of labels,y
is an original value,y
with hat  predicted one  accuracy → const MetricType

A classification metric. The formula is
where
k
is a number of correctly predicted labels,n
 total amount of labelsThe greater the score produced by the metric, the better the prediction's quality is. The metric produces scores within the range
0, 1
 precision → const MetricType

A classification metric. The formula for a singleclass problem is
where
TP
is a number of correctly predicted positive labels (true positive),FP
 a number of incorrectly predicted positive labels (false positive). In other words,TP + FP
is a number of all the labels predicted to be positiveThe formula for a multiclass problem is
Where
Score 1..t
are scores for each class from 1 to tThe greater the score produced by the metric, the better the prediction's quality is. The metric produces scores within the range
0, 1
 recall → const MetricType

A classification metric. The formula for a singleclass problem is
where
TP
is a number of correctly predicted positive labels (true positive),FN
 a number of incorrectly predicted negative labels (false negative). In other words,TP + FN
is a total amount of positive labels for a class in the given dataThe formula for a multiclass problem is
Where
Score 1..t
are scores for each class from 1 to tThe greater the score produced by the metric, the better the prediction's quality is. The metric produces scores within the range
0, 1
Properties
Methods

noSuchMethod(
Invocation invocation) → dynamic 
Invoked when a nonexistent method or property is accessed.
inherited

toString(
) → String 
A string representation of this object.
inherited
Operators

operator ==(
Object other) → bool 
The equality operator.
inherited
Constants

values
→ const List<
MetricType>  A constant List of the values in this enum, in order of their declaration.