Metrics¶
The metric of a usecase is the function that will be used to assess for the efficiency of its models. The metrics you can choose depends on the type of usecase you are training.
-
class
previsionio.metrics.
Classification
¶ Metrics for classification projects Available metrics in prevision: auc, log_loss, error_rate_binary
-
AUC
= 'auc'¶ Area Under ROC Curve
-
AUCPR
= 'aucpr'¶ precision recall area under the curve score
-
F05
= 'F05'¶ F05 Score
-
F1
= 'F1'¶ Balanced F-score
-
F2
= 'F2'¶ F2 Score
-
F3
= 'F3'¶ F3 Score
-
F4
= 'F4'¶ F4 Score
-
Lift01
= 'lift_at_0.1'¶ lift at ratio 0.1
-
Lift02
= 'lift_at_0.2'¶ lift at ratio 0.2
-
Lift03
= 'lift_at_0.3'¶ lift at ratio 0.3
-
Lift04
= 'lift_at_0.4'¶ lift at ratio 0.4
-
Lift05
= 'lift_at_0.5'¶ lift at ratio 0.5
-
Lift06
= 'lift_at_0.6'¶ lift at ratio 0.6
-
Lift07
= 'lift_at_0.7'¶ lift at ratio 0.7
-
Lift08
= 'lift_at_0.8'¶ lift at ratio 0.8
-
Lift09
= 'lift_at_0.9'¶ lift at ratio 0.9
-
MCC
= 'mcc'¶ Matthews correlation coefficient
-
accuracy
= 'accuracy'¶ Accuracy
-
error_rate
= 'error_rate_binary'¶ Error rate
-
gini
= 'gini'¶ Gini score
-
log_loss
= 'log_loss'¶ Logarithmic Loss
-
-
class
previsionio.metrics.
MultiClassification
¶ Metrics for multiclassification projects
-
AUC
= 'auc'¶ Area Under ROC Curve
-
MAP10
= 'map_at_10'¶ qmean average precision @10
-
MAP3
= 'map_at_3'¶ qmean average precision @3
-
MAP5
= 'map_at_5'¶ qmean average precision @5
-
accuracy
= 'accuracy'¶ accuracy
-
error_rate
= 'error_rate_multi'¶ Multi-class Error rate
-
log_loss
= 'log_loss'¶ Logarithmic Loss
-
macroF1
= 'macroF1'¶ balanced F-score
-
qkappa
= 'qkappa'¶ quadratic weighted kappa
-
-
class
previsionio.metrics.
Regression
¶ Metrics for regression projects Available metrics in prevision: rmse, mape, rmsle, mse, mae
-
MAE
= 'mae'¶ Mean Average Error
-
MAPE
= 'mape'¶ Mean Average Percentage Error
-
MER
= 'mer'¶ Median Absolute Error
-
MSE
= 'mse'¶ Mean squared Error
-
R2
= 'R2'¶ R2 Error
-
RMSE
= 'rmse'¶ Root Mean Squared Error
-
RMSLE
= 'rmsle'¶ Root Mean Squared Logarithmic Error
-
RMSPE
= 'rmspe'¶ Root Mean Squared Percentage Error
-
SMAPE
= 'smape'¶ Symmetric Mean Absolute Percentage Error
-