verticapy.machine_learning.vertica.ensemble.RandomForestRegressor¶

class verticapy.machine_learning.vertica.ensemble.RandomForestRegressor(name: str = None, overwrite_model: bool = False, n_estimators: int = 10, max_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: Annotated[int | float | Decimal, 'Python Numbers'] = 1000000000.0, sample: float = 0.632, max_depth: int = 5, min_samples_leaf: int = 1, min_info_gain: Annotated[int | float | Decimal, 'Python Numbers'] = 0.0, nbins: int = 32)¶

Creates a RandomForestRegressor object using the Vertica RF_REGRESSOR function. It is an ensemble learning method for regression that operates by constructing a multitude of decision trees at training-time and outputting a class with the mode.

Parameters¶

name: str, optional

Name of the model. The model is stored in the DB.

overwrite_model: bool, optional

If set to True, training a model with the same name as an existing model overwrites the existing model.

n_estimators: int, optional

The number of trees in the forest, an integer between 1 and 1000, inclusive.

max_features: int | str, optional

The number of randomly chosen features from which to pick the best feature to split a given tree node. It can be an integer or one of the two following methods.

auto:
square root of the total number of predictors.
max :
number of predictors.

max_leaf_nodes: PythonNumber, optional

The maximum number of leaf nodes for a tree in the forest, an integerv between ``1 and 1e9, inclusive.

sample: float, optional

The portion of the input data set that is randomly selected for training each tree, a float between 0.0 and 1.0, inclusive.

max_depth: int, optional

aximum depth of each tree, an integer between 1 and 100, inclusive.

min_samples_leaf: int, optional

The minimum number of samples each branch must have after splitting a node, an integer between 1 and 1e6, inclusive. A split that results in remaining samples less than this value is discarded.

min_info_gain: PythonNumber, optional

The minimum threshold for including a split, a float between 0.0 and 1.0, inclusive. A split with information gain less than this threshold is discarded.

nbins: int, optional

Number of bins used to find splits in each column, where more splits leads to a longer runtime but more fine-grained, possibly better splits. Must be an integer between 2 and 1000, inclusive.

Attributes¶

Many attributes are created during the fitting phase.

trees_: list of BinaryTreeRegressor: Tree models are instances of ` BinaryTreeRegressor, each possessing various attributes. For more detailed information, refer to the documentation for BinaryTreeRegressor.
features_importance_: numpy.array: The importance of features. It is calculated using the MDI (Mean Decreased Impurity). To determine the final score, VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them. It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.
features_importance_trees_: dict of numpy.array: Each element of the array represents the feature importance of tree i. The importance of features is calculated using the MDI (Mean Decreased Impurity). It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.
n_estimators_: int: The number of model estimators.

Note

All attributes can be accessed using the get_attributes() method.

Note

Several other attributes can be accessed by using the get_vertica_attributes() method.

Examples¶

The following examples provide a basic understanding of usage. For more detailed examples, please refer to the Machine Learning or the Examples section on the website.

Important

Many tree-based models inherit from the RandomForest base class, and it’s recommended to use it directly for access to a wider range of options.

Load data for machine learning¶

We import verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

For this example, we will use the winequality dataset.

import verticapy.datasets as vpd

data = vpd.load_winequality()

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	3.8	0.31	0.02	11.1	0.036	20.0	114.0	0.99248	3.75	0.44	12.4	6	0	white
2	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
3	4.2	0.17	0.36	1.8	0.029	93.0	161.0	0.98999	3.65	0.89	12.0	7	1	white
4	4.2	0.215	0.23	5.1	0.041	64.0	157.0	0.99688	3.42	0.44	8.0	3	0	white
5	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white
6	4.4	0.46	0.1	2.8	0.024	31.0	111.0	0.98816	3.48	0.34	13.1	6	0	white
7	4.4	0.54	0.09	5.1	0.038	52.0	97.0	0.99022	3.41	0.4	12.2	7	1	white
8	4.5	0.19	0.21	0.95	0.033	89.0	159.0	0.99332	3.34	0.42	8.0	5	0	white
9	4.6	0.445	0.0	1.4	0.053	11.0	178.0	0.99426	3.79	0.55	10.2	5	0	white
10	4.6	0.52	0.15	2.1	0.054	8.0	65.0	0.9934	3.9	0.56	13.1	4	0	red
11	4.7	0.145	0.29	1.0	0.042	35.0	90.0	0.9908	3.76	0.49	11.3	6	0	white
12	4.7	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.5	5	0	white
13	4.7	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
14	4.7	0.6	0.17	2.3	0.058	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red
15	4.7	0.67	0.09	1.0	0.02	5.0	9.0	0.98722	3.3	0.34	13.6	5	0	white
16	4.7	0.785	0.0	3.4	0.036	23.0	134.0	0.98981	3.53	0.92	13.8	6	0	white
17	4.8	0.13	0.32	1.2	0.042	40.0	98.0	0.9898	3.42	0.64	11.8	7	1	white
18	4.8	0.17	0.28	2.9	0.03	22.0	111.0	0.9902	3.38	0.34	11.3	7	1	white
19	4.8	0.21	0.21	10.2	0.037	17.0	112.0	0.99324	3.66	0.48	12.2	7	1	white
20	4.8	0.225	0.38	1.2	0.074	47.0	130.0	0.99132	3.31	0.4	10.3	6	0	white
21	4.8	0.26	0.23	10.6	0.034	23.0	111.0	0.99274	3.46	0.28	11.5	7	1	white
22	4.8	0.29	0.23	1.1	0.044	38.0	180.0	0.98924	3.28	0.34	11.9	6	0	white
23	4.8	0.33	0.0	6.5	0.028	34.0	163.0	0.9937	3.35	0.61	9.9	5	0	white
24	4.8	0.34	0.0	6.5	0.028	33.0	163.0	0.9939	3.36	0.61	9.9	6	0	white
25	4.8	0.65	0.12	1.1	0.013	4.0	10.0	0.99246	3.32	0.36	13.5	4	0	white
26	4.9	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6	0	white
27	4.9	0.33	0.31	1.2	0.016	39.0	150.0	0.98713	3.33	0.59	14.0	8	1	white
28	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white
29	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white
30	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
31	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
32	4.9	0.42	0.0	2.1	0.048	16.0	42.0	0.99154	3.71	0.74	14.0	7	1	red
33	4.9	0.47	0.17	1.9	0.035	60.0	148.0	0.98964	3.27	0.35	11.5	6	0	white
34	5.0	0.17	0.56	1.5	0.026	24.0	115.0	0.9906	3.48	0.39	10.8	7	1	white
35	5.0	0.2	0.4	1.9	0.015	20.0	98.0	0.9897	3.37	0.55	12.05	6	0	white
36	5.0	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6	0	white
37	5.0	0.24	0.19	5.0	0.043	17.0	101.0	0.99438	3.67	0.57	10.0	5	0	white
38	5.0	0.24	0.21	2.2	0.039	31.0	100.0	0.99098	3.69	0.62	11.7	6	0	white
39	5.0	0.24	0.34	1.1	0.034	49.0	158.0	0.98774	3.32	0.32	13.1	7	1	white
40	5.0	0.255	0.22	2.7	0.043	46.0	153.0	0.99238	3.75	0.76	11.3	6	0	white
41	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
42	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
43	5.0	0.27	0.4	1.2	0.076	42.0	124.0	0.99204	3.32	0.47	10.1	6	0	white
44	5.0	0.29	0.54	5.7	0.035	54.0	155.0	0.98976	3.27	0.34	12.9	8	1	white
45	5.0	0.3	0.33	3.7	0.03	54.0	173.0	0.9887	3.36	0.3	13.0	7	1	white
46	5.0	0.31	0.0	6.4	0.046	43.0	166.0	0.994	3.3	0.63	9.9	6	0	white
47	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
48	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
49	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
50	5.0	0.33	0.18	4.6	0.032	40.0	124.0	0.99114	3.18	0.4	11.0	6	0	white
51	5.0	0.33	0.23	11.8	0.03	23.0	158.0	0.99322	3.41	0.64	11.8	6	0	white
52	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white
53	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white
54	5.0	0.38	0.01	1.6	0.048	26.0	60.0	0.99084	3.7	0.75	14.0	6	0	red
55	5.0	0.4	0.5	4.3	0.046	29.0	80.0	0.9902	3.49	0.66	13.6	6	0	red
56	5.0	0.42	0.24	2.0	0.06	19.0	50.0	0.9917	3.72	0.74	14.0	8	1	red
57	5.0	0.44	0.04	18.6	0.039	38.0	128.0	0.9985	3.37	0.57	10.2	6	0	white
58	5.0	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
59	5.0	0.55	0.14	8.3	0.032	35.0	164.0	0.9918	3.53	0.51	12.5	8	1	white
60	5.0	0.61	0.12	1.3	0.009	65.0	100.0	0.9874	3.26	0.37	13.5	5	0	white
61	5.0	0.74	0.0	1.2	0.041	16.0	46.0	0.99258	4.01	0.59	12.5	6	0	red
62	5.0	1.02	0.04	1.4	0.045	41.0	85.0	0.9938	3.75	0.48	10.5	4	0	red
63	5.0	1.04	0.24	1.6	0.05	32.0	96.0	0.9934	3.74	0.62	11.5	5	0	red
64	5.1	0.11	0.32	1.6	0.028	12.0	90.0	0.99008	3.57	0.52	12.2	6	0	white
65	5.1	0.14	0.25	0.7	0.039	15.0	89.0	0.9919	3.22	0.43	9.2	6	0	white
66	5.1	0.165	0.22	5.7	0.047	42.0	146.0	0.9934	3.18	0.55	9.9	6	0	white
67	5.1	0.21	0.28	1.4	0.047	48.0	148.0	0.99168	3.5	0.49	10.4	5	0	white
68	5.1	0.23	0.18	1.0	0.053	13.0	99.0	0.98956	3.22	0.39	11.5	5	0	white
69	5.1	0.25	0.36	1.3	0.035	40.0	78.0	0.9891	3.23	0.64	12.1	7	1	white
70	5.1	0.26	0.33	1.1	0.027	46.0	113.0	0.98946	3.35	0.43	11.4	7	1	white
71	5.1	0.26	0.34	6.4	0.034	26.0	99.0	0.99449	3.23	0.41	9.2	6	0	white
72	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white
73	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white
74	5.1	0.3	0.3	2.3	0.048	40.0	150.0	0.98944	3.29	0.46	12.2	6	0	white
75	5.1	0.305	0.13	1.75	0.036	17.0	73.0	0.99	3.4	0.51	12.3333333333333	5	0	white
76	5.1	0.31	0.3	0.9	0.037	28.0	152.0	0.992	3.54	0.56	10.1	6	0	white
77	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
78	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
79	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
80	5.1	0.33	0.27	6.7	0.022	44.0	129.0	0.99221	3.36	0.39	11.0	7	1	white
81	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
82	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
83	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
84	5.1	0.39	0.21	1.7	0.027	15.0	72.0	0.9894	3.5	0.45	12.5	6	0	white
85	5.1	0.42	0.0	1.8	0.044	18.0	88.0	0.99157	3.68	0.73	13.6	7	1	red
86	5.1	0.42	0.01	1.5	0.017	25.0	102.0	0.9894	3.38	0.36	12.3	7	1	white
87	5.1	0.47	0.02	1.3	0.034	18.0	44.0	0.9921	3.9	0.62	12.8	6	0	red
88	5.1	0.51	0.18	2.1	0.042	16.0	101.0	0.9924	3.46	0.87	12.9	7	1	red
89	5.1	0.52	0.06	2.7	0.052	30.0	79.0	0.9932	3.32	0.43	9.3	5	0	white
90	5.1	0.585	0.0	1.7	0.044	14.0	86.0	0.99264	3.56	0.94	12.9	7	1	red
91	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white
92	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white
93	5.2	0.16	0.34	0.8	0.029	26.0	77.0	0.99155	3.25	0.51	10.1	6	0	white
94	5.2	0.17	0.27	0.7	0.03	11.0	68.0	0.99218	3.3	0.41	9.8	5	0	white
95	5.2	0.185	0.22	1.0	0.03	47.0	123.0	0.99218	3.55	0.44	10.15	6	0	white
96	5.2	0.2	0.27	3.2	0.047	16.0	93.0	0.99235	3.44	0.53	10.1	7	1	white
97	5.2	0.21	0.31	1.7	0.048	17.0	61.0	0.98953	3.24	0.37	12.0	7	1	white
98	5.2	0.22	0.46	6.2	0.066	41.0	187.0	0.99362	3.19	0.42	9.73333333333333	5	0	white
99	5.2	0.24	0.15	7.1	0.043	32.0	134.0	0.99378	3.24	0.48	9.9	6	0	white
100	5.2	0.24	0.45	3.8	0.027	21.0	128.0	0.992	3.55	0.49	11.2	8	1	white

Rows: 1-100 | Columns: 14

Note

VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.

You can easily divide your dataset into training and testing subsets using the vDataFrame.train_test_split() method. This is a crucial step when preparing your data for machine learning, as it allows you to evaluate the performance of your models accurately.

data = vpd.load_winequality()
train, test = data.train_test_split(test_size = 0.2)

Warning

In this case, VerticaPy utilizes seeded randomization to guarantee the reproducibility of your data split. However, please be aware that this approach may lead to reduced performance. For a more efficient data split, you can use the vDataFrame.to_db() method to save your results into tables or temporary tables. This will help enhance the overall performance of the process.

Model Initialization¶

First we import the RandomForestRegressor model:

from verticapy.machine_learning.vertica import RandomForestRegressor

Then we can create the model:

model = RandomForestRegressor(
    max_features = "auto",
    max_leaf_nodes = 32,
    sample = 0.5,
    max_depth = 3,
    min_samples_leaf = 5,
    min_info_gain = 0.0,
    nbins = 32,
)

Hint

In verticapy 1.0.x and higher, you do not need to specify the model name, as the name is automatically assigned. If you need to re-use the model, you can fetch the model name from the model’s attributes.

Important

The model name is crucial for the model management system and versioning. It’s highly recommended to provide a name if you plan to reuse the model later.

Model Training¶

We can now fit the model:

model.fit(
    train,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density",
    ],
    "quality",
    test,
)



===========
call_string
===========
SELECT rf_regressor('"public"."_verticapy_tmp_randomforestregressor_v_demo_d1d33a5455a311ef880f0242ac120002_"', '"public"."_verticapy_tmp_view_v_demo_d1e048de55a311ef880f0242ac120002_"', 'quality', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"' USING PARAMETERS exclude_columns='', ntree=10, mtry=3, sampling_size=0.5, max_depth=3, max_breadth=32, min_leaf_size=5, min_info_gain=0, nbins=32);

=======
details
=======
   predictor    |      type      
----------------+----------------
 fixed_acidity  |float or numeric
volatile_acidity|float or numeric
  citric_acid   |float or numeric
 residual_sugar |float or numeric
   chlorides    |float or numeric
    density     |float or numeric


===============
Additional Info
===============
       Name       |Value
------------------+-----
    tree_count    | 10  
rejected_row_count|  0  
accepted_row_count|5192 

Important

To train a model, you can directly use the vDataFrame or the name of the relation stored in the database. The test set is optional and is only used to compute the test metrics. In verticapy, we don’t work using X matrices and y vectors. Instead, we work directly with lists of predictors and the response name.

Features Importance¶

We can conveniently get the features importance:

result = model.features_importance()

Note

In models such as RandomForest, feature importance is calculated using the MDI (Mean Decreased Impurity). To determine the final score, VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them.

Metrics¶

We can get the entire report using:

model.report()

	value
explained_variance	0.172685468333178
max_error	2.81184858072756
median_absolute_error	0.568571830498591
mean_absolute_error	0.624029016229468
mean_squared_error	0.610582506103535
root_mean_squared_error	0.78139778992747
r2	0.171809995860813
r2_adj	0.167981690756934
aic	-629.649201401192
bic	-593.593405267752

Rows: 1-10 | Columns: 2

Important

Most metrics are computed using a single SQL query, but some of them might require multiple SQL queries. Selecting only the necessary metrics in the report can help optimize performance. E.g. model.report(metrics = ["mse", "r2"]).

You can utilize the score() function to calculate various regression metrics, with the R-squared being the default.

model.score()
Out[4]: 0.171809995860813

Prediction¶

Prediction is straight-forward:

model.predict(
    test,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density",
    ],
    "prediction",
)

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 prediction Float(22)
1	3.8	0.31	0.02	11.1	0.036	20.0	114.0	0.99248	3.75	0.44	12.4	6	0	white	5.64612149752263
2	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white	6.40536563044835
3	4.4	0.54	0.09	5.1	0.038	52.0	97.0	0.99022	3.41	0.4	12.2	7	1	white	6.42153335103855
4	4.5	0.19	0.21	0.95	0.033	89.0	159.0	0.99332	3.34	0.42	8.0	5	0	white	5.65991849887665
5	4.7	0.145	0.29	1.0	0.042	35.0	90.0	0.9908	3.76	0.49	11.3	6	0	white	6.22059586371688
6	4.7	0.6	0.17	2.3	0.058	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red	5.27183283829591
7	4.8	0.21	0.21	10.2	0.037	17.0	112.0	0.99324	3.66	0.48	12.2	7	1	white	5.71383655348769
8	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white	5.61420883859505
9	4.9	0.42	0.0	2.1	0.048	16.0	42.0	0.99154	3.71	0.74	14.0	7	1	red	5.82728618790732
10	5.0	0.24	0.21	2.2	0.039	31.0	100.0	0.99098	3.69	0.62	11.7	6	0	white	6.23676358430708
11	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white	6.40536563044835
12	5.0	0.27	0.4	1.2	0.076	42.0	124.0	0.99204	3.32	0.47	10.1	6	0	white	5.68000591388113
13	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white	5.86540259296358
14	5.0	0.33	0.18	4.6	0.032	40.0	124.0	0.99114	3.18	0.4	11.0	6	0	white	6.38927732246673
15	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white	5.77099544710246
16	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white	5.77099544710246
17	5.0	0.44	0.04	18.6	0.039	38.0	128.0	0.9985	3.37	0.57	10.2	6	0	white	5.62828607302533
18	5.0	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white	6.31622061319733
19	5.0	0.74	0.0	1.2	0.041	16.0	46.0	0.99258	4.01	0.59	12.5	6	0	red	5.51864670498941
20	5.1	0.14	0.25	0.7	0.039	15.0	89.0	0.9919	3.22	0.43	9.2	6	0	white	6.23676358430708
21	5.1	0.25	0.36	1.3	0.035	40.0	78.0	0.9891	3.23	0.64	12.1	7	1	white	6.30005289260713
22	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white	5.97294677993842
23	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white	5.97294677993842
24	5.1	0.3	0.3	2.3	0.048	40.0	150.0	0.98944	3.29	0.46	12.2	6	0	white	5.99506581279796
25	5.1	0.31	0.3	0.9	0.037	28.0	152.0	0.992	3.54	0.56	10.1	6	0	white	5.94103412101083
26	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white	6.31622061319733
27	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white	6.31622061319733
28	5.1	0.33	0.27	6.7	0.022	44.0	129.0	0.99221	3.36	0.39	11.0	7	1	white	5.94103412101083
29	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white	6.45057781635967
30	5.1	0.39	0.21	1.7	0.027	15.0	72.0	0.9894	3.5	0.45	12.5	6	0	white	6.31622061319733
31	5.1	0.51	0.18	2.1	0.042	16.0	101.0	0.9924	3.46	0.87	12.9	7	1	red	5.7105812119026
32	5.1	0.52	0.06	2.7	0.052	30.0	79.0	0.9932	3.32	0.43	9.3	5	0	white	5.45053454938001
33	5.1	0.585	0.0	1.7	0.044	14.0	86.0	0.99264	3.56	0.94	12.9	7	1	red	5.60502448276719
34	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white	6.30005289260713
35	5.2	0.22	0.46	6.2	0.066	41.0	187.0	0.99362	3.19	0.42	9.73333333333333	5	0	white	5.87022924082098
36	5.2	0.24	0.15	7.1	0.043	32.0	134.0	0.99378	3.24	0.48	9.9	6	0	white	5.62828607302533
37	5.2	0.31	0.36	5.1	0.031	46.0	145.0	0.9897	3.14	0.31	12.4	7	1	white	6.40536563044835
38	5.2	0.44	0.04	1.4	0.036	43.0	119.0	0.9894	3.36	0.33	12.1	8	1	white	6.31622061319733
39	5.2	0.645	0.0	2.15	0.08	15.0	28.0	0.99444	3.78	0.61	12.5	6	0	red	5.40530832036396
40	5.3	0.21	0.29	0.7	0.028	11.0	66.0	0.99215	3.3	0.4	9.8	5	0	white	6.04579767817583
41	5.3	0.3	0.16	4.2	0.029	37.0	100.0	0.9905	3.3	0.36	11.8	8	1	white	6.38927732246673
42	5.3	0.31	0.38	10.5	0.031	53.0	140.0	0.99321	3.34	0.46	11.7	6	0	white	5.97294677993842
43	5.3	0.31	0.38	10.5	0.031	53.0	140.0	0.99321	3.34	0.46	11.7	6	0	white	5.97294677993842
44	5.3	0.76	0.03	2.7	0.043	27.0	93.0	0.9932	3.34	0.38	9.2	5	0	white	5.51864670498941
45	5.4	0.15	0.32	2.5	0.037	10.0	51.0	0.98878	3.04	0.58	12.6	6	0	white	6.30005289260713
46	5.4	0.18	0.24	4.8	0.041	30.0	113.0	0.99445	3.42	0.4	9.4	6	0	white	5.72931793535308
47	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white	6.30005289260713
48	5.4	0.31	0.47	3.0	0.053	46.0	144.0	0.9931	3.29	0.76	10.0	5	0	white	5.69830113217635
49	5.4	0.33	0.31	4.0	0.03	27.0	108.0	0.99031	3.3	0.43	12.2	7	1	white	6.40536563044835
50	5.4	0.375	0.4	3.3	0.054	29.0	147.0	0.99482	3.42	0.52	9.1	5	0	white	5.66018472712009
51	5.4	0.46	0.15	2.1	0.026	29.0	130.0	0.98953	3.39	0.77	13.4	8	1	white	6.31622061319733
52	5.4	0.5	0.13	5.0	0.028	12.0	107.0	0.99079	3.48	0.88	13.5	7	1	white	6.38927732246673
53	5.4	0.58	0.08	1.9	0.059	20.0	31.0	0.99484	3.5	0.64	10.2	6	0	red	5.42360353865918
54	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white	6.22059586371688
55	5.5	0.17	0.23	2.9	0.039	10.0	108.0	0.99243	3.28	0.5	10.0	5	0	white	5.78479244845648
56	5.5	0.23	0.19	2.2	0.044	39.0	161.0	0.99209	3.19	0.43	10.4	6	0	white	5.73908278817488
57	5.5	0.335	0.3	2.5	0.071	27.0	128.0	0.9924	3.14	0.51	9.6	6	0	white	5.68000591388113
58	5.5	0.42	0.09	1.6	0.019	18.0	68.0	0.9906	3.33	0.51	11.4	7	1	white	6.23676358430708
59	5.6	0.18	0.27	1.7	0.03	31.0	103.0	0.98892	3.35	0.37	12.9	6	0	white	6.30005289260713
60	5.6	0.18	0.58	1.25	0.034	29.0	129.0	0.98984	3.51	0.6	12.0	7	1	white	6.30005289260713
61	5.6	0.19	0.39	1.1	0.043	17.0	67.0	0.9918	3.23	0.53	10.3	6	0	white	6.22059586371688
62	5.6	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white	5.71877843344105
63	5.6	0.21	0.4	1.3	0.041	81.0	147.0	0.9901	3.22	0.95	11.6	8	1	white	6.30005289260713
64	5.6	0.235	0.29	1.2	0.047	33.0	127.0	0.991	3.34	0.5	11.0	7	1	white	5.91560878390772
65	5.6	0.24	0.34	2.0	0.041	14.0	73.0	0.98981	3.04	0.45	11.6	7	1	white	6.30005289260713
66	5.6	0.25	0.26	3.6	0.037	18.0	115.0	0.9904	3.42	0.5	12.6	6	0	white	6.37310960187653
67	5.6	0.255	0.57	10.7	0.056	66.0	171.0	0.99464	3.25	0.61	10.4	7	1	white	5.69830113217635
68	5.6	0.26	0.5	11.4	0.029	25.0	93.0	0.99428	3.23	0.49	10.5	6	0	white	5.83023740586128
69	5.6	0.28	0.27	3.9	0.043	52.0	158.0	0.99202	3.35	0.44	10.7	7	1	white	5.94103412101083
70	5.6	0.29	0.05	0.8	0.038	11.0	30.0	0.9924	3.36	0.35	9.2	5	0	white	5.73908278817488
71	5.6	0.295	0.2	2.2	0.049	18.0	134.0	0.99378	3.21	0.68	10.0	5	0	white	5.51715253070855
72	5.6	0.31	0.78	13.9	0.074	23.0	92.0	0.99677	3.39	0.48	10.5	6	0	red	5.61601680633055
73	5.6	0.35	0.14	5.0	0.046	48.0	198.0	0.9937	3.3	0.71	10.3	5	0	white	5.68360827507148
74	5.6	0.39	0.24	4.7	0.034	27.0	77.0	0.9906	3.28	0.36	12.7	5	0	white	6.38927732246673
75	5.6	0.42	0.34	2.4	0.022	34.0	97.0	0.98915	3.22	0.38	12.8	7	1	white	6.30005289260713
76	5.6	0.46	0.24	4.8	0.042	24.0	72.0	0.9908	3.29	0.37	12.6	6	0	white	6.38927732246673
77	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white	6.38927732246673
78	5.6	0.62	0.03	1.5	0.08	6.0	13.0	0.99498	3.66	0.62	10.1	4	0	red	5.40530832036396
79	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red	5.40530832036396
80	5.7	0.1	0.27	1.3	0.047	21.0	100.0	0.9928	3.27	0.46	9.5	5	0	white	5.8885244591162
81	5.7	0.12	0.26	5.5	0.034	21.0	99.0	0.99324	3.09	0.57	9.9	6	0	white	6.04579767817583
82	5.7	0.16	0.32	1.2	0.036	7.0	89.0	0.99111	3.26	0.48	11.0	5	0	white	6.22059586371688
83	5.7	0.2	0.24	13.8	0.047	44.0	112.0	0.99837	2.97	0.66	8.8	6	0	white	5.71877843344105
84	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white	5.69600112899039
85	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white	5.69600112899039
86	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white	5.69600112899039
87	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white	5.69600112899039
88	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white	5.69600112899039
89	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white	5.69600112899039
90	5.7	0.245	0.33	1.1	0.049	28.0	150.0	0.9927	3.13	0.42	9.3	5	0	white	5.69830113217635
91	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white	5.4782249640689
92	5.7	0.26	0.25	10.4	0.02	7.0	57.0	0.994	3.39	0.37	10.6	5	0	white	5.62828607302533
93	5.7	0.265	0.28	6.9	0.036	46.0	150.0	0.99299	3.36	0.44	10.8	7	1	white	5.97294677993842
94	5.7	0.265	0.28	6.9	0.036	46.0	150.0	0.99299	3.36	0.44	10.8	7	1	white	5.97294677993842
95	5.7	0.32	0.18	1.4	0.029	26.0	104.0	0.9906	3.44	0.37	11.0	6	0	white	6.23676358430708
96	5.7	0.37	0.3	1.1	0.029	24.0	88.0	0.98883	3.18	0.39	11.7	6	0	white	6.30005289260713
97	5.7	0.385	0.04	12.6	0.034	22.0	115.0	0.9964	3.28	0.63	9.9	6	0	white	5.62828607302533
98	5.7	0.46	0.46	1.4	0.04	31.0	169.0	0.9932	3.13	0.47	8.8	5	0	white	5.91253254473856
99	5.8	0.12	0.21	1.3	0.056	35.0	121.0	0.9908	3.32	0.33	11.4	6	0	white	5.93042307241773
100	5.8	0.13	0.26	5.1	0.039	19.0	103.0	0.99478	3.36	0.47	9.3	6	0	white	5.99032316507243

Rows: 1-100 | Columns: 15

Note

Predictions can be made automatically using the test set, in which case you don’t need to specify the predictors. Alternatively, you can pass only the vDataFrame to the predict() function, but in this case, it’s essential that the column names of the vDataFrame match the predictors and response name in the model.

Plots¶

Tree models can be visualized by drawing their tree plots. For more examples, check out Machine Learning - Tree Plots.

model.plot_tree()

../_images/machine_learning_vertica_rfreg.png

Note

The above example may not render properly in the doc because of the huge size of the tree. But it should render nicely in jupyter environment.

In order to plot graph using graphviz separately, you can extract the graphviz DOT file code as follows:

model.to_graphviz()
Out[5]: 'digraph Tree {\ngraph [bgcolor="#FFFFFFDD"];\n0 [label="\\"chlorides\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n0 -> 1 [label="<= 0.046625", color="#000000", fontcolor="#000000"]\n0 -> 2 [label="> 0.046625", color="#000000", fontcolor="#000000"]\n1 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n1 -> 3 [label="<= 7.55625", color="#000000", fontcolor="#000000"]\n1 -> 4 [label="> 7.55625", color="#000000", fontcolor="#000000"]\n2 [label="\\"chlorides\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n2 -> 5 [label="<= 0.065437", color="#000000", fontcolor="#000000"]\n2 -> 6 [label="> 0.065437", color="#000000", fontcolor="#000000"]\n3 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n3 -> 7 [label="<= 0.595625", color="#000000", fontcolor="#000000"]\n3 -> 8 [label="> 0.595625", color="#000000", fontcolor="#000000"]\n4 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n4 -> 9 [label="<= 0.991991", color="#000000", fontcolor="#000000"]\n4 -> 10 [label="> 0.991991", color="#000000", fontcolor="#000000"]\n5 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n5 -> 11 [label="<= 6.459375", color="#000000", fontcolor="#000000"]\n5 -> 12 [label="> 6.459375", color="#000000", fontcolor="#000000"]\n6 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n6 -> 13 [label="<= 9.384375", color="#000000", fontcolor="#000000"]\n6 -> 14 [label="> 9.384375", color="#000000", fontcolor="#000000"]\n7 [label="6.086", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n8 [label="5.222222", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n9 [label="6.058824", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n10 [label="5.586592", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n11 [label="5.567568", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n12 [label="5.728543", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n13 [label="5.384615", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n14 [label="5.832117", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n}'

This string can then be copied into a DOT file which can beparsed by graphviz.

Contour plot is another useful plot that can be produced for models with two predictors.

model.contour()

Important

Machine learning models with two predictors can usually benefit from their own contour plot. This visual representation aids in exploring predictions and gaining a deeper understanding of how these models perform in different scenarios. Please refer to Contour Plot for more examples.

Model Register¶

In order to register the model for tracking and versioning:

model.register("model_v1")

Please refer to Model Tracking and Versioning for more details on model tracking and versioning.

Model Exporting¶

To Memmodel

model.to_memmodel()

Note

MemModel objects serve as in-memory representations of machine learning models. They can be used for both in-database and in-memory prediction tasks. These objects can be pickled in the same way that you would pickle a scikit-learn model.

The following methods for exporting the model use MemModel, and it is recommended to use MemModel directly.

To SQL

You can get the SQL code by:

model.to_sql()
Out[6]: '((CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "fixed_acidity" < 7.55625 THEN (CASE WHEN "volatile_acidity" < 0.595625 THEN 6.086 ELSE 5.222222 END) ELSE (CASE WHEN "density" < 0.991991 THEN 6.058824 ELSE 5.586592 END) END) ELSE (CASE WHEN "chlorides" < 0.065437 THEN (CASE WHEN "fixed_acidity" < 6.459375 THEN 5.567568 ELSE 5.728543 END) ELSE (CASE WHEN "fixed_acidity" < 9.384375 THEN 5.384615 ELSE 5.832117 END) END) END) + (CASE WHEN "volatile_acidity" < 0.54875 THEN (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "density" < 0.993611 THEN 6.257802 ELSE 5.703057 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 5.471545 ELSE 5.750323 END) END) ELSE (CASE WHEN "fixed_acidity" < 10.846875 THEN (CASE WHEN "citric_acid" < 0.466875 THEN 5.27 ELSE 4.833333 END) ELSE 6.0 END) END) + (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN (CASE WHEN "volatile_acidity" < 0.501875 THEN 6.236413 ELSE 5.25 END) ELSE (CASE WHEN "density" < 0.990371 THEN 6.87037 ELSE 6.492308 END) END) ELSE (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "citric_acid" < 0.259375 THEN 5.529412 ELSE 5.941957 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 5.934783 ELSE 5.506824 END) END) END) + (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN (CASE WHEN "fixed_acidity" < 6.825 THEN 6.324444 ELSE 6.0 END) ELSE (CASE WHEN "residual_sugar" < 6.7125 THEN 6.6 ELSE 7.055556 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "residual_sugar" < 8.75 THEN 5.942446 ELSE 6.1625 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 5.408784 ELSE 5.675676 END) END) END) + (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "density" < 0.990371 THEN (CASE WHEN "volatile_acidity" < 0.54875 THEN 6.526316 ELSE 5.666667 END) ELSE (CASE WHEN "residual_sugar" < 2.6375 THEN 6.013699 ELSE 6.581818 END) END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "fixed_acidity" < 4.996875 THEN 4.2 ELSE 5.448739 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 6.044619 ELSE 5.6537 END) END) END) + (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "fixed_acidity" < 7.55625 THEN (CASE WHEN "density" < 0.991991 THEN 6.418708 ELSE 5.890815 END) ELSE (CASE WHEN "fixed_acidity" < 9.384375 THEN 5.724138 ELSE 4.941176 END) END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "volatile_acidity" < 0.970625 THEN 5.404255 ELSE 4.090909 END) ELSE (CASE WHEN "density" < 0.995232 THEN 5.898601 ELSE 5.647986 END) END) END) + (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN (CASE WHEN "density" < 0.990371 THEN 6.373239 ELSE 6.091286 END) ELSE 6.516854 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "citric_acid" < 0.259375 THEN 5.58 ELSE 6.046512 END) ELSE (CASE WHEN "volatile_acidity" < 0.455 THEN 5.656566 ELSE 5.37155 END) END) END) + (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "residual_sugar" < 6.7125 THEN (CASE WHEN "fixed_acidity" < 7.55625 THEN 6.21791 ELSE 5.761905 END) ELSE (CASE WHEN "density" < 0.993611 THEN 6.537037 ELSE 5.664688 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "fixed_acidity" < 8.2875 THEN 6.040724 ELSE 5.666667 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 5.390519 ELSE 5.614085 END) END) END) + (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "fixed_acidity" < 8.2875 THEN 6.426501 ELSE 5.363636 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 5.4375 ELSE 5.896721 END) END) ELSE (CASE WHEN "density" < 0.995232 THEN (CASE WHEN "volatile_acidity" < 0.36125 THEN 5.859425 ELSE 5.478261 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 6.12037 ELSE 5.470149 END) END) END) + (CASE WHEN "volatile_acidity" < 0.54875 THEN (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "density" < 0.991991 THEN 6.294872 ELSE 5.4573 END) ELSE (CASE WHEN "chlorides" < 0.046625 THEN 6.133195 ELSE 5.800244 END) END) ELSE (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 5.375 ELSE 6.666667 END) ELSE (CASE WHEN "citric_acid" < 0.155625 THEN 5.389535 ELSE 5.120567 END) END) END)) / 10'

To Python

To obtain the prediction function in Python syntax, use the following code:

X = [[4.2, 0.17, 0.36, 1.8, 0.029, 0.9899]]

model.to_python()(X)
Out[8]: array([6.3000528])

Hint

The to_python() method is used to retrieve predictions, probabilities, or cluster distances. For specific details on how to use this method for different model types, refer to the relevant documentation for each model.

__init__(name: str = None, overwrite_model: bool = False, n_estimators: int = 10, max_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: Annotated[int | float | Decimal, 'Python Numbers'] = 1000000000.0, sample: float = 0.632, max_depth: int = 5, min_samples_leaf: int = 1, min_info_gain: Annotated[int | float | Decimal, 'Python Numbers'] = 0.0, nbins: int = 32) → None¶: Must be overridden in the child class

Methods

`__init__`([name, overwrite_model, ...])	Must be overridden in the child class
`contour`([nbins, chart])	Draws the model's contour plot.
`deploySQL`([X])	Returns the SQL code needed to deploy the model.
`does_model_exists`(name[, raise_error, ...])	Checks whether the model is stored in the Vertica database.
`drop`()	Drops the model from the Vertica database.
`export_models`(name, path[, kind])	Exports machine learning models.
`features_importance`([tree_id, show, chart])	Computes the model's features importance.
`fit`(input_relation, X, y[, test_relation, ...])	Trains the model.
`get_attributes`([attr_name])	Returns the model attributes.
`get_match_index`(x, col_list[, str_check])	Returns the matching index.
`get_params`()	Returns the parameters of the model.
`get_plotting_lib`([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`get_score`([tree_id])	Returns the feature importance metrics for the input tree.
`get_tree`([tree_id])	Returns a table with all the input tree information.
`get_vertica_attributes`([attr_name])	Returns the model Vertica attributes.
`import_models`(path[, schema, kind])	Imports machine learning models.
`plot`([max_nb_points, chart])	Draws the model.
`plot_tree`([tree_id, pic_path])	Draws the input tree.
`predict`(vdf[, X, name, inplace])	Predicts using the input relation.
`register`(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`regression_report`([metrics])	Computes a regression report
`report`([metrics])	Computes a regression report
`score`([metric])	Computes the model score.
`set_params`([parameters])	Sets the parameters of the model.
`summarize`()	Summarizes the model.
`to_binary`(path)	Exports the model to the Vertica Binary format.
`to_graphviz`([tree_id, classes_color, ...])	Returns the code for a Graphviz tree.
`to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`to_pmml`(path)	Exports the model to PMML.
`to_python`([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`to_sql`([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes

object_type