Model.score¶

In [ ]:

Model.score(method: str = "accuracy",
            pos_label: Union[int, float, str] = None,
            cutoff: float = -1,
            nbins: int = 10000,)

Computes the model score.

Parameters¶

Name	Type	Optional	Description
method	str	✓	The method to use to compute the score. accuracy : Accuracy auc : Area Under the Curve (ROC) best_cutoff : Cutoff which optimised the ROC Curve prediction. bm : Informedness = tpr + tnr - 1 csi : Critical Success Index = tp / (tp + fn + fp) f1 : F1 Score logloss : Log Loss mcc : Matthews Correlation Coefficient mk : Markedness = ppv + npv - 1 npv : Negative Predictive Value = tn / (tn + fn) prc_auc : Area Under the Curve (PRC) precision : Precision = tp / (tp + fp) specificity : Specificity = tn / (tn + fp)
pos_label	int / float / str	✓	Label to consider as positive. All the other classes will be merged and considered as negative in case of multi classification.
cutoff	float	✓	Cutoff for which the tested category will be accepted as prediction. If the parameter is not between 0 and 1, an automatic cutoff is computed.
nbins	int	✓	[Only when method is set to auc\|prc_auc\|best_cutoff] An integer value that determines the number of decision boundaries. Decision boundaries are set at equally spaced intervals between 0 and 1, inclusive. Greater values for nbins give more precise estimations of the AUC, but can potentially decrease performance. The maximum value is 999,999. If negative, the maximum value is used.

Returns¶

float : score

Example¶

In [26]:

# Multiclass Classification
from verticapy.learn.ensemble import RandomForestClassifier
model = RandomForestClassifier(name = "public.RF_iris",
                               n_estimators = 20,
                               max_features = "auto",
                               max_leaf_nodes = 32, 
                               sample = 0.7,
                               max_depth = 3,
                               min_samples_leaf = 5,
                               min_info_gain = 0.0,
                               nbins = 32)
model.fit("public.iris", ["PetalLengthCm", "PetalWidthCm"], "Species")
# Logloss
model.score(method = "log_loss", pos_label = "Iris-virginica")

Out[26]:

0.037429015840143

In [27]:

# Global accuracy
model.score(method = "accuracy")

Out[27]:

0.946666666666667