Model.regression_report / report¶

In [ ]:

Model.report(method: str = "metrics")

Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).

Parameters¶

Name	Type	Optional	Description
method	str	✓	The method to use to compute the score. anova : Computes the model ANOVA table. details : Computes the model details. metrics : Computes the model different metrics.

Returns¶

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

Example¶

In [7]:

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.drop()
model.fit("public.winequality", ["alcohol", "fixed_acidity"], "quality")
# metrics
model.report()

Out[7]:

	value
explained_variance	0.200278059718928
max_error	3.42100345960034
median_absolute_error	0.536239695275772
mean_absolute_error	0.637335798291648
mean_squared_error	0.60986700452594
root_mean_squared_error	0.7809398213216816
r2	0.200129728147483
r2_adj	0.19988338682569295
aic	-3206.8598680821424
bic	-3186.522580649501

Rows: 1-10 | Columns: 2

In [9]:

# ANOVA
model.report("anova")

Out[9]:

	Df	SS	MS	F	p_value
Regression	2	829.977510545617	414.9887552728085	687.0575887418752	2.1083023654898362e-271
Residual	6494	3922.43244365662	0.6040086916625531
Total	6496	4953.68570109281

Rows: 1-3 | Columns: 6

In [10]:

# details
model.report("details")

Out[10]:

	value
Dep. Variable	"quality"
Model	RandomForestRegressor
No. Observations	6497.0
No. Predictors	2
R-squared	0.208178984227579
Adj. R-squared	0.2079351218882588
F-statistic	687.0575887418752
Prob (F-statistic)	2.1083023654898362e-271
Kurtosis	0.232322269343305
Skewness	0.189622693372695
Jarque-Bera (JB)	53.1115447611131

Rows: 1-11 | Columns: 2