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	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
2	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
3	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
4	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
5	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
6	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
7	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
8	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
9	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
10	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
11	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
12	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
13	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
14	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
15	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
16	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
17	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
18	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
19	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
20	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
21	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
22	5.2	0.31	0.2	2.4	0.027	27.0	117.0	0.98886	3.56	0.45	13.0	7	1	white
23	5.2	0.32	0.25	1.8	0.103	13.0	50.0	0.9957	3.38	0.55	9.2	5	0	red
24	5.2	0.34	0.37	6.2	0.031	42.0	133.0	0.99076	3.25	0.41	12.5	6	0	white
25	5.2	0.36	0.02	1.6	0.031	24.0	104.0	0.9896	3.44	0.35	12.2	6	0	white
26	5.2	0.365	0.08	13.5	0.041	37.0	142.0	0.997	3.46	0.39	9.9	6	0	white
27	5.2	0.48	0.04	1.6	0.054	19.0	106.0	0.9927	3.54	0.62	12.2	7	1	red
28	5.2	0.5	0.18	2.0	0.036	23.0	129.0	0.98949	3.36	0.77	13.4	7	1	white
29	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
30	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
31	5.3	0.165	0.24	1.1	0.051	25.0	105.0	0.9925	3.32	0.47	9.1	5	0	white
32	5.3	0.23	0.56	0.9	0.041	46.0	141.0	0.99119	3.16	0.62	9.7	5	0	white
33	5.3	0.3	0.3	1.2	0.029	25.0	93.0	0.98742	3.31	0.4	13.6	7	1	white
34	5.3	0.33	0.3	1.2	0.048	25.0	119.0	0.99045	3.32	0.62	11.3	6	0	white
35	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
36	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
37	5.3	0.4	0.25	3.9	0.031	45.0	130.0	0.99072	3.31	0.58	11.75	7	1	white
38	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.6	7	1	red
39	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.5666666666667	7	1	red
40	5.3	0.715	0.19	1.5	0.161	7.0	62.0	0.99395	3.62	0.61	11.0	5	0	red
41	5.4	0.22	0.29	1.2	0.045	69.0	152.0	0.99178	3.76	0.63	11.0	7	1	white
42	5.4	0.595	0.1	2.8	0.042	26.0	80.0	0.9932	3.36	0.38	9.3	5	0	white
43	5.4	0.74	0.09	1.7	0.089	16.0	26.0	0.99402	3.67	0.56	11.6	6	0	red
44	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
45	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
46	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
47	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
48	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.65	6	0	white
49	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.6333333333333	6	0	white
50	5.5	0.18	0.22	5.5	0.037	10.0	86.0	0.99156	3.46	0.44	12.2	5	0	white
51	5.5	0.24	0.45	1.7	0.046	22.0	113.0	0.99224	3.22	0.48	10.0	5	0	white
52	5.5	0.29	0.3	1.1	0.022	20.0	110.0	0.98869	3.34	0.38	12.8	7	1	white
53	5.5	0.31	0.29	3.0	0.027	16.0	102.0	0.99067	3.23	0.56	11.2	6	0	white
54	5.5	0.32	0.45	4.9	0.028	25.0	191.0	0.9922	3.51	0.49	11.5	7	1	white
55	5.5	0.35	0.35	1.1	0.045	14.0	167.0	0.992	3.34	0.68	9.9	6	0	white
56	5.5	0.375	0.38	1.7	0.036	17.0	98.0	0.99142	3.29	0.39	10.5	6	0	white
57	5.6	0.15	0.26	5.55	0.051	51.0	139.0	0.99336	3.47	0.5	11.0	6	0	white
58	5.6	0.15	0.31	5.3	0.038	8.0	79.0	0.9923	3.3	0.39	10.5	6	0	white
59	5.6	0.16	0.27	1.4	0.044	53.0	168.0	0.9918	3.28	0.37	10.1	6	0	white
60	5.6	0.175	0.29	0.8	0.043	20.0	67.0	0.99112	3.28	0.48	9.9	6	0	white
61	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
62	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
63	5.6	0.22	0.32	1.2	0.024	29.0	97.0	0.98823	3.2	0.46	13.05	7	1	white
64	5.6	0.26	0.18	1.4	0.034	18.0	135.0	0.99174	3.32	0.35	10.2	6	0	white
65	5.6	0.26	0.26	5.7	0.031	12.0	80.0	0.9923	3.25	0.38	10.8	5	0	white
66	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
67	5.6	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white
68	5.6	0.3	0.1	6.4	0.043	34.0	142.0	0.99382	3.14	0.48	9.8	5	0	white
69	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
70	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
71	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
72	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
73	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
74	5.7	0.15	0.47	11.4	0.035	49.0	128.0	0.99456	3.03	0.34	10.5	8	1	white
75	5.7	0.18	0.26	2.2	0.023	21.0	95.0	0.9893	3.07	0.54	12.3	6	0	white
76	5.7	0.18	0.36	1.2	0.046	9.0	71.0	0.99199	3.7	0.68	10.9	7	1	white
77	5.7	0.2	0.3	2.5	0.046	38.0	125.0	0.99276	3.34	0.5	9.9	6	0	white
78	5.7	0.21	0.32	0.9	0.038	38.0	121.0	0.99074	3.24	0.46	10.6	6	0	white
79	5.7	0.21	0.37	4.5	0.04	58.0	140.0	0.99332	3.29	0.62	10.6	6	0	white
80	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
81	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
82	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
83	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
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
85	5.7	0.22	0.29	3.5	0.04	27.0	146.0	0.98999	3.17	0.36	12.1	6	0	white
86	5.7	0.23	0.28	9.65	0.025	26.0	121.0	0.9925	3.28	0.38	11.3	6	0	white
87	5.7	0.25	0.26	12.5	0.049	52.5	106.0	0.99691	3.08	0.45	9.4	6	0	white
88	5.7	0.25	0.26	12.5	0.049	52.5	120.0	0.99691	3.08	0.45	9.4	6	0	white
89	5.7	0.25	0.27	11.5	0.04	24.0	120.0	0.99411	3.33	0.31	10.8	6	0	white
90	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
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
92	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
93	5.7	0.27	0.32	1.2	0.046	20.0	155.0	0.9934	3.8	0.41	10.2	6	0	white
94	5.7	0.28	0.24	17.5	0.044	60.0	167.0	0.9989	3.31	0.44	9.4	5	0	white
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
96	5.7	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white
97	5.7	0.36	0.34	4.2	0.026	21.0	77.0	0.9907	3.41	0.45	11.9	6	0	white
98	5.8	0.14	0.15	6.1	0.042	27.0	123.0	0.99362	3.06	0.6	9.9	6	0	white
99	5.8	0.15	0.32	1.2	0.037	14.0	119.0	0.99137	3.19	0.5	10.2	6	0	white
100	5.8	0.17	0.34	1.8	0.045	96.0	170.0	0.99035	3.38	0.9	11.8	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_mldb_463d98f4979811efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_466ba51e979811efa8720242ac120002_"', '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|5199 

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	0.177671305427858
max_error	3.48732126843369
median_absolute_error	0.523595651178165
mean_absolute_error	0.624306886536957
mean_squared_error	0.613954715094571
root_mean_squared_error	0.78355262433009
r2	0.177352751392407
r2_adj	0.17352944892018
aic	-619.045880801741
bic	-583.028612218568

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]: 0.177352751392407

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	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	6.51441254699719
2	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	6.32814432680877
3	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	6.5981092271823
4	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	6.32772823883528
5	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	6.51577648698276
6	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	6.51577648698276
7	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	5.42389897018353
8	5.3	0.2	0.31	3.6	0.036	22.0	91.0	0.99278	3.41	0.5	9.8	6	0	white	5.99314353314607
9	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	6.48362458824858
10	5.4	0.59	0.07	7.0	0.045	36.0	147.0	0.9944	3.34	0.57	9.7	6	0	white	5.42389897018353
11	5.5	0.16	0.26	1.5	0.032	35.0	100.0	0.99076	3.43	0.77	12.0	6	0	white	6.20135149054618
12	5.6	0.18	0.3	10.2	0.028	28.0	131.0	0.9954	3.49	0.42	10.8	7	1	white	5.90789982997621
13	5.6	0.19	0.26	1.4	0.03	12.0	76.0	0.9905	3.25	0.37	10.9	7	1	white	6.20135149054618
14	5.6	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white	6.41657502290236
15	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red	5.50389897018353
16	5.6	0.605	0.05	2.4	0.073	19.0	25.0	0.99258	3.56	0.55	12.9	5	0	red	5.49695788274742
17	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	5.82670422945635
18	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	5.80414808466225
19	5.7	0.31	0.29	7.3	0.05	33.0	143.0	0.99332	3.31	0.5	11.0666666666667	6	0	white	5.82670422945635
20	5.7	0.36	0.21	6.7	0.038	51.0	166.0	0.9941	3.29	0.63	10.0	6	0	white	5.45944453515454
21	5.8	0.19	0.49	4.9	0.04	44.0	118.0	0.9935	3.34	0.38	9.5	7	1	white	5.97058738835197
22	5.8	0.27	0.26	3.5	0.071	26.0	69.0	0.98994	3.1	0.38	11.5	6	0	white	6.48103923181336
23	5.8	0.28	0.27	2.6	0.054	30.0	156.0	0.9914	3.53	0.42	12.4	5	0	white	6.13716146321643
24	5.8	0.28	0.34	4.0	0.031	40.0	99.0	0.9896	3.39	0.39	12.8	7	1	white	6.5981092271823
25	5.8	0.3	0.12	1.6	0.036	57.0	163.0	0.99239	3.38	0.59	10.5	6	0	white	5.60434046154874
26	5.8	0.33	0.2	16.05	0.047	26.0	166.0	0.9976	3.09	0.46	8.9	5	0	white	5.43422806144518
27	5.8	0.34	0.16	7.0	0.037	26.0	116.0	0.9949	3.46	0.45	10.0	7	1	white	5.45944453515454
28	5.8	0.345	0.15	10.8	0.033	26.0	120.0	0.99494	3.25	0.49	10.0	6	0	white	5.47640434882184
29	5.8	0.36	0.26	3.3	0.038	40.0	153.0	0.9911	3.34	0.55	11.3	6	0	white	6.51577648698276
30	5.8	0.38	0.26	1.1	0.058	20.0	140.0	0.99271	3.27	0.43	9.7	6	0	white	5.53250344771844
31	5.8	0.555	0.26	4.5	0.053	17.0	126.0	0.9943	3.24	0.46	9.1	5	0	white	5.42389897018353
32	5.9	0.2	0.23	1.5	0.037	38.0	93.0	0.99021	3.36	0.49	12.0	6	0	white	6.41657502290236
33	5.9	0.23	0.28	8.6	0.046	37.0	142.0	0.99432	3.23	0.53	10.6	6	0	white	5.78819917194081
34	5.9	0.24	0.28	1.3	0.032	36.0	95.0	0.98889	3.08	0.64	12.9	7	1	white	6.41657502290236
35	5.9	0.27	0.29	11.4	0.036	31.0	115.0	0.9949	3.35	0.48	10.5	8	1	white	5.8051589856081
36	5.9	0.29	0.25	13.4	0.067	72.0	160.0	0.99721	3.33	0.54	10.3	6	0	red	5.43422806144518
37	5.9	0.34	0.31	2.0	0.03	38.0	142.0	0.98892	3.4	0.41	12.9	7	1	white	6.44503024145143
38	6.0	0.18	0.31	1.4	0.036	14.0	75.0	0.99085	3.34	0.58	11.1	8	1	white	6.20135149054618
39	6.0	0.2	0.32	3.0	0.031	26.0	118.0	0.99134	3.38	0.68	11.2	7	1	white	6.48732126843369
40	6.0	0.24	0.34	1.0	0.036	52.0	184.0	0.99097	3.44	0.44	11.45	6	0	white	6.20135149054618
41	6.0	0.24	0.41	1.3	0.036	42.0	118.0	0.99018	3.04	0.64	11.75	6	0	white	6.41657502290236
42	6.1	0.24	0.25	1.6	0.044	24.0	115.0	0.9921	3.39	0.59	10.9	6	0	white	5.63855309245956
43	6.1	0.24	0.26	1.7	0.033	61.0	134.0	0.9903	3.19	0.81	11.9	7	1	white	6.41657502290236
44	6.1	0.28	0.24	19.95	0.074	32.0	174.0	0.99922	3.19	0.44	9.3	6	0	white	5.43422806144518
45	6.1	0.31	0.26	2.2	0.051	28.0	167.0	0.9926	3.37	0.47	10.4	6	0	white	5.53250344771844
46	6.1	0.34	0.46	4.7	0.029	21.0	94.0	0.991	3.29	0.62	12.3	6	0	white	6.51577648698276
47	6.1	0.35	0.07	1.4	0.069	22.0	108.0	0.9934	3.23	0.52	9.2	5	0	white	5.53250344771844
48	6.1	0.38	0.14	3.9	0.06	27.0	113.0	0.99344	3.07	0.34	9.2	4	0	white	5.53250344771844
49	6.1	0.4	0.16	1.8	0.069	11.0	25.0	0.9955	3.42	0.74	10.1	7	1	red	5.41726824777789
50	6.1	0.41	0.0	1.6	0.063	36.0	87.0	0.9914	3.27	0.67	10.8	6	0	white	6.16561668176549
51	6.1	0.48	0.09	1.7	0.078	18.0	30.0	0.99402	3.45	0.54	11.2	6	0	red	5.39630835184664
52	6.2	0.19	0.29	4.3	0.045	33.0	126.0	0.99658	3.18	0.42	9.3	6	0	white	5.86588417343366
53	6.2	0.2	0.25	15.0	0.055	8.0	120.0	0.99767	3.19	0.53	9.6	6	0	white	5.78173759334401
54	6.2	0.24	0.25	12.5	0.055	47.0	134.0	0.99758	3.3	0.51	9.0	5	0	white	5.468440692356
55	6.2	0.24	0.28	12.2	0.049	54.0	133.0	0.9952	3.19	0.37	10.7	6	0	white	5.8051589856081
56	6.2	0.26	0.37	7.1	0.047	54.0	201.0	0.99523	3.19	0.48	9.5	6	0	white	5.69022662947124
57	6.2	0.29	0.29	5.6	0.046	35.0	178.0	0.99313	3.25	0.51	10.5333333333333	5	0	white	5.89854124328665
58	6.2	0.34	0.25	12.1	0.059	33.0	171.0	0.99769	3.14	0.56	8.7	6	0	white	5.43422806144518
59	6.2	0.37	0.24	6.1	0.032	19.0	86.0	0.98934	3.04	0.26	13.4	8	1	white	6.62656444573137
60	6.2	0.46	0.25	4.4	0.066	62.0	207.0	0.9939	3.25	0.52	9.8	5	0	white	5.39630835184664
61	6.2	0.46	0.29	2.1	0.074	32.0	98.0	0.99578	3.33	0.62	9.8	5	0	red	5.66155820729459
62	6.2	0.51	0.14	1.9	0.056	15.0	34.0	0.99396	3.48	0.57	11.5	6	0	red	5.39630835184664
63	6.3	0.17	0.42	2.8	0.028	45.0	107.0	0.9908	3.27	0.43	11.8	6	0	white	6.48732126843369
64	6.3	0.2	0.26	1.6	0.027	36.0	141.0	0.99268	3.53	0.56	10.8	6	0	white	5.87020731111694
65	6.3	0.21	0.28	1.5	0.051	46.0	142.0	0.9928	3.23	0.42	10.1	6	0	white	5.92130651931577
66	6.3	0.23	0.3	1.8	0.033	16.0	91.0	0.9906	3.28	0.4	11.8	6	0	white	6.20135149054618
67	6.3	0.25	0.44	11.6	0.041	48.0	195.0	0.9968	3.18	0.52	9.5	5	0	white	5.70718644313853
68	6.3	0.26	0.29	2.2	0.043	35.0	175.0	0.9918	3.38	0.43	11.6	6	0	white	6.20135149054618
69	6.3	0.27	0.18	7.7	0.048	45.0	186.0	0.9962	3.23	0.47	9.0	5	0	white	5.41726824777789
70	6.3	0.27	0.23	2.9	0.047	13.0	100.0	0.9936	3.28	0.43	9.8	5	0	white	5.45944453515454
71	6.3	0.28	0.29	6.8	0.051	40.0	143.0	0.99374	3.43	0.59	11.0	6	0	white	5.78819917194081
72	6.3	0.39	0.24	6.9	0.069	9.0	117.0	0.9942	3.15	0.35	10.2	4	0	white	5.45944453515454
73	6.3	0.41	0.16	0.9	0.032	25.0	98.0	0.99274	3.16	0.42	9.5	5	0	white	5.55689272266468
74	6.3	0.48	0.04	1.1	0.046	30.0	99.0	0.9928	3.24	0.36	9.6	6	0	white	5.54120427824084
75	6.3	0.55	0.15	1.8	0.077	26.0	35.0	0.99314	3.32	0.82	11.6	6	0	red	5.46936726441054
76	6.3	0.68	0.01	3.7	0.103	32.0	54.0	0.99586	3.51	0.66	11.3	6	0	red	5.40157980335405
77	6.4	0.125	0.36	1.4	0.044	22.0	68.0	0.99014	3.15	0.5	11.7	7	1	white	6.41657502290236
78	6.4	0.15	0.29	1.8	0.044	21.0	115.0	0.99166	3.1	0.38	10.2	5	0	white	6.20135149054618
79	6.4	0.15	0.36	1.8	0.034	43.0	150.0	0.9922	3.42	0.69	11.0	8	1	white	5.99314353314607
80	6.4	0.18	0.32	9.6	0.052	24.0	90.0	0.9963	3.35	0.49	9.4	6	0	white	5.88343141532857
81	6.4	0.19	0.35	10.2	0.043	40.0	106.0	0.99632	3.16	0.5	9.7	6	0	white	5.88343141532857
82	6.4	0.2	0.15	6.6	0.046	26.0	113.0	0.99408	2.99	0.58	9.9	6	0	white	5.72531138472275
83	6.4	0.2	0.28	2.5	0.032	24.0	84.0	0.99168	3.31	0.55	11.5	5	0	white	6.20135149054618
84	6.4	0.23	0.3	7.1	0.037	63.0	236.0	0.9952	3.06	0.34	9.2	6	0	white	5.78819917194081
85	6.4	0.23	0.37	7.9	0.05	60.0	150.0	0.99488	2.86	0.49	9.3	6	0	white	5.76373075729317
86	6.4	0.26	0.25	10.7	0.046	66.0	179.0	0.99606	3.17	0.55	9.9	6	0	white	5.468440692356
87	6.4	0.27	0.29	10.8	0.028	17.0	118.0	0.99356	3.18	0.37	11.2	6	0	white	5.89854124328665
88	6.4	0.27	0.32	4.5	0.24	61.0	174.0	0.9948	3.12	0.48	9.4	5	0	white	5.7046760375488
89	6.4	0.32	0.5	10.7	0.047	57.0	206.0	0.9968	3.08	0.6	9.4	5	0	white	5.68463029834443
90	6.4	0.4	0.25	4.2	0.032	15.0	91.0	0.98988	3.26	0.52	13.1	6	0	white	6.62656444573137
91	6.4	0.42	0.46	8.4	0.05	58.0	180.0	0.99495	3.18	0.46	9.7	6	0	white	5.71628301840912
92	6.4	0.45	0.07	1.1	0.03	10.0	131.0	0.9905	2.97	0.28	10.8	5	0	white	6.22980670909524
93	6.4	0.55	0.26	9.6	0.027	20.0	104.0	0.9924	3.22	0.73	13.1	6	0	white	5.54120427824084
94	6.5	0.08	0.33	1.9	0.028	23.0	93.0	0.991	3.34	0.7	12.0	7	1	white	6.20135149054618
95	6.5	0.17	0.54	8.5	0.082	64.0	163.0	0.9959	2.89	0.39	8.8	6	0	white	5.84391545686718
96	6.5	0.19	0.28	1.4	0.046	22.0	90.0	0.99038	3.18	0.51	11.7	7	1	white	6.20135149054618
97	6.5	0.19	0.34	1.6	0.029	39.0	116.0	0.98954	3.21	0.68	12.5	6	0	white	6.41657502290236
98	6.5	0.27	0.19	4.2	0.046	6.0	114.0	0.9955	3.25	0.35	8.6	4	0	white	5.41726824777789
99	6.5	0.27	0.19	6.6	0.045	98.0	175.0	0.99364	3.16	0.34	10.1	6	0	white	5.45944453515454
100	6.5	0.27	0.4	10.0	0.039	74.0	227.0	0.99582	3.18	0.5	9.4	5	0	white	5.70718644313853

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="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n0 -> 1 [label="<= 0.991973", color="#000000", fontcolor="#000000"]\n0 -> 2 [label="> 0.991973", color="#000000", fontcolor="#000000"]\n1 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n1 -> 3 [label="<= 7.959375", color="#000000", fontcolor="#000000"]\n1 -> 4 [label="> 7.959375", color="#000000", fontcolor="#000000"]\n2 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n2 -> 5 [label="<= 0.995215", color="#000000", fontcolor="#000000"]\n2 -> 6 [label="> 0.995215", color="#000000", fontcolor="#000000"]\n3 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n3 -> 7 [label="<= 0.314375", color="#000000", fontcolor="#000000"]\n3 -> 8 [label="> 0.314375", color="#000000", fontcolor="#000000"]\n4 [label="5.75", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n5 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n5 -> 9 [label="<= 0.408125", color="#000000", fontcolor="#000000"]\n5 -> 10 [label="> 0.408125", color="#000000", fontcolor="#000000"]\n6 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n6 -> 11 [label="<= 0.220625", color="#000000", fontcolor="#000000"]\n6 -> 12 [label="> 0.220625", color="#000000", fontcolor="#000000"]\n7 [label="6.345506", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n8 [label="6.630058", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n9 [label="5.899851", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n10 [label="5.425373", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n11 [label="6.043956", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n12 [label="5.478088", 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 /notebooks/ml/model_tracking_versioning/index.ipynb 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 "density" < 0.991973 THEN (CASE WHEN "fixed_acidity" < 7.959375 THEN (CASE WHEN "volatile_acidity" < 0.314375 THEN 6.345506 ELSE 6.630058 END) ELSE 5.75 END) ELSE (CASE WHEN "density" < 0.995215 THEN (CASE WHEN "volatile_acidity" < 0.408125 THEN 5.899851 ELSE 5.425373 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 6.043956 ELSE 5.478088 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN (CASE WHEN "density" < 0.990352 THEN 6.409722 ELSE 6.096234 END) ELSE (CASE WHEN "fixed_acidity" < 7.58125 THEN 6.719745 ELSE 6.166667 END) END) ELSE (CASE WHEN "density" < 0.993594 THEN (CASE WHEN "citric_acid" < 0.269062 THEN 5.562963 ELSE 6.041379 END) ELSE (CASE WHEN "residual_sugar" < 8.75 THEN 5.556405 ELSE 5.726003 END) END) END) + (CASE WHEN "density" < 0.993594 THEN (CASE WHEN "chlorides" < 0.046562 THEN (CASE WHEN "density" < 0.990352 THEN 6.558824 ELSE 6.157983 END) ELSE (CASE WHEN "density" < 0.991973 THEN 6.0625 ELSE 5.717241 END) END) ELSE (CASE WHEN "citric_acid" < 0.269062 THEN (CASE WHEN "volatile_acidity" < 0.876875 THEN 5.411348 ELSE 4.7 END) ELSE (CASE WHEN "citric_acid" < 0.3075 THEN 5.930435 ELSE 5.685751 END) END) END) + (CASE WHEN "density" < 0.993594 THEN (CASE WHEN "density" < 0.990352 THEN (CASE WHEN "chlorides" < 0.046562 THEN 6.572254 ELSE 6.166667 END) ELSE (CASE WHEN "chlorides" < 0.046562 THEN 6.143608 ELSE 5.865979 END) END) ELSE (CASE WHEN "citric_acid" < 0.269062 THEN (CASE WHEN "volatile_acidity" < 0.970625 THEN 5.447842 ELSE 4.416667 END) ELSE (CASE WHEN "chlorides" < 0.140469 THEN 5.752709 ELSE 5.162162 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "volatile_acidity" < 0.54875 THEN (CASE WHEN "fixed_acidity" < 7.58125 THEN 6.451271 ELSE 6.044776 END) ELSE 5.692308 END) ELSE (CASE WHEN "citric_acid" < 0.269062 THEN (CASE WHEN "volatile_acidity" < 0.2675 THEN 5.693431 ELSE 5.351304 END) ELSE (CASE WHEN "density" < 0.995215 THEN 5.983395 ELSE 5.670116 END) END) END) + (CASE WHEN "volatile_acidity" < 0.455 THEN (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 6.265416 ELSE 6.721519 END) ELSE (CASE WHEN "citric_acid" < 0.269062 THEN 5.480978 ELSE 5.855462 END) END) ELSE (CASE WHEN "fixed_acidity" < 6.06875 THEN (CASE WHEN "volatile_acidity" < 0.501875 THEN 6.4 ELSE 5.6 END) ELSE (CASE WHEN "density" < 0.990352 THEN 6.666667 ELSE 5.324094 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "chlorides" < 0.027781 THEN 6.592105 ELSE (CASE WHEN "chlorides" < 0.046562 THEN 6.336585 ELSE 6.067797 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 6.008639 ELSE (CASE WHEN "citric_acid" < 0.269062 THEN 5.354633 ELSE 5.684043 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "density" < 0.990352 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 6.458599 ELSE 6.836364 END) ELSE (CASE WHEN "residual_sugar" < 2.6375 THEN 6.099567 ELSE 6.557971 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "residual_sugar" < 6.7125 THEN 5.947598 ELSE 6.198157 END) ELSE (CASE WHEN "citric_acid" < 0.269062 THEN 5.374429 ELSE 5.701736 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN (CASE WHEN "density" < 0.990352 THEN 6.29932 ELSE 6.017778 END) ELSE (CASE WHEN "chlorides" < 0.027781 THEN 6.511628 ELSE 6.666667 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 6.04878 ELSE (CASE WHEN "citric_acid" < 0.269062 THEN 5.375963 ELSE 5.673216 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN (CASE WHEN "density" < 0.990352 THEN 6.468254 ELSE 6.099567 END) ELSE (CASE WHEN "fixed_acidity" < 6.825 THEN 6.772358 ELSE 6.361702 END) END) ELSE (CASE WHEN "citric_acid" < 0.269062 THEN (CASE WHEN "volatile_acidity" < 0.220625 THEN 5.758242 ELSE 5.341693 END) ELSE (CASE WHEN "citric_acid" < 0.46125 THEN 5.84474 ELSE 5.619178 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([6.4165751])

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