The metric of an experiment 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 experiment 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'


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'


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

class previsionio.metrics.TextSimilarity

Metrics for text similarity projects

accuracy_at_k = 'accuracy_at_k'

Accuracy at K

mrr_at_k = 'mrr_at_k'

Mean Reciprocal Rank at K