Model.score¶

In [ ]:

Model.score(method: str = "r2")

Scores the model.

Parameters¶

Name	Type	Optional	Description
method	str	✓	The method to use to score the model. aic : Akaike’s Information Criterion bic : Bayesian Information Criterion max : Max Error mae : Mean Absolute Error median : Median Absolute Error mse : Mean Squared Error msle : Mean Squared Log Error r2 : R-squared coefficient r2a : R2 adjusted rmse : Root-mean-square Error var : Explained variance

Returns¶

float : score

Example¶

In [34]:

from verticapy.learn.ensemble import RandomForestRegressor
model = RandomForestRegressor(name = "public.RF_winequality",
                              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.winequality", 
          ["alcohol", "fixed_acidity", "pH", "density"], 
          "quality")
# R2
model.score(method = "r2")

Out[34]:

0.216696276202655

In [35]:

# MSLE
model.score(method = "msle")

Out[35]:

0.00252843985480557

In [36]:

# Mean Absolute Error
model.score(method = "mae")

Out[36]:

0.626849198590535