load_model¶

In [ ]:

load_model(name: str, 
           input_relation: str = "",
           test_relation: str = "")

Loads a Vertica model and returns the associated object.

Parameters¶

Name	Type	Optional	Description
name	str	❌	Model Name.
input_relation	str	✓	Some automated functions may depend on the input relation. If the load_model function could not find the input relation from the call string, it is important to manually fill it.
test_relation	str	✓	Relation to use to do the testing. All the methods will use this relation for the scoring. If empty, the training relation will be used as testing.

Returns¶

model : The model.

Example¶

In [2]:

from verticapy.learn.tools import *
model = load_model("logit_titanic")
model

Out[2]:


=======
details
=======
 predictor |coefficient|std_err |z_value |p_value 
-----------+-----------+--------+--------+--------
 Intercept |  0.27678  | 0.43558| 0.63541| 0.52516
    age    | -0.01807  | 0.00626|-2.88476| 0.00392
family_size| -0.02921  | 0.05317|-0.54926| 0.58283
   fare    |  0.01571  | 0.00329| 4.77443| 0.00000
  pclass   | -0.05925  | 0.12269|-0.48292| 0.62915
    sex    | -0.10181  | 0.16029|-0.63519| 0.52530


==============
regularization
==============
type| lambda 
----+--------
 l2 | 1.00000


===========
call_string
===========
logistic_reg('public.logit_titanic', '"public".VERTICAPY_SPLIT_titanicclean_67_TRAIN', '"survived"', '"age", "family_size", "fare", "pclass", "sex"'
USING PARAMETERS optimizer='cgd', epsilon=0.0001, max_iterations=100, regularization='l2', lambda=1, alpha=0)

===============
Additional Info
===============
       Name       |Value
------------------+-----
 iteration_count  |  4  
rejected_row_count|  0  
accepted_row_count| 840