Model.report¶

In [ ]:

Model.report()

Computes a regression/classification report using multiple metrics to evaluate the model depending on its type.

Returns¶

tablesample : An object containing the result. For more information, see utilities.tablesample.

Example¶

In [10]:

from verticapy.learn.preprocessing import StandardScaler
from verticapy.learn.ensemble import RandomForestRegressor
from verticapy.learn.pipeline import Pipeline

model1 = StandardScaler("public.Std_winequality")
model2 = 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 = Pipeline([("WineSTD", model1), ("WineRF", model2)])
model.drop()
model.fit("public.winequality", ["alcohol", "fixed_acidity"], "quality")
model.report()

Out[10]:

	value
explained_variance	0.188597167882303
max_error	3.39595012120849
median_absolute_error	0.550179653999915
mean_absolute_error	0.6405965242475
mean_squared_error	0.618664737209411
root_mean_squared_error	0.7865524376730461
r2	0.188591073357194
r2_adj	0.1883411783998048

Rows: 1-8 | Columns: 2