DummyTreeRegressor¶
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DummyTreeRegressor(name: str)
A regressor that overfits the training data. These models are typically used as a control to compare with your other models.
Parameters¶
| Name | Type | Optional | Description |
|---|---|---|---|
name | str | ❌ | Name of the model to be stored in the database. |
Attributes¶
After the object is created, all parameters become attributes. Additional attributes will be created when fitting the model:
| Name | Type | Description |
|---|---|---|
input_relation | str | Training relation. |
X | list | List of the predictors. |
y | str | Response column. |
test_relation | str | Relation to use to test the model. All model methods are abstractions that simplify the process. The testing relation will be used by the methods to evaluate the model. If empty, the training relation will be used instead. This attribute can be changed at any time. |
Methods¶
| Name | Description |
|---|---|
| contour | Draws the model's contour plot. |
| deploySQL | Returns the SQL code needed to deploy the model. |
| drop | Drops the model from the Vertica DB. |
| features_importance | Computes the model features importance using the Gini Index. |
| fit | Trains the model. |
| get_attr | Returns the model attribute. |
| get_params | Returns the model Parameters. |
| get_tree | Returns a tablesample with all the input tree information. |
| plot | Draws the Model. |
| plot_tree | Draws the input tree (requires the graphviz module). |
| predict | Predicts using the input relation. |
| regression_report / report | Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...). |
| score | Computes the model score. |
| set_params | Sets the parameters of the model. |
| to_graphviz | Converts the input tree to a Graphviz tree. |
| to_memmodel | Converts a specified Vertica model to a memModel model. |
| to_python | Returns the Python code needed to deploy the model without using built-in Vertica functions. |
| to_sql | Returns the SQL code needed to deploy the model without using Vertica built-in functions. |
Example¶
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from verticapy.learn.tree import DummyTreeRegressor
model = DummyTreeRegressor(name = "public.rf_winequality")
display(model)