LinearRegression¶
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LinearRegression(name: str,
cursor = None,
tol: float = 1e-6,
max_iter: int = 100,
solver: str = 'Newton')
Creates a LinearRegression object with the Vertica linear regression function.
Parameters¶
| Name | Type | Optional | Description |
|---|---|---|---|
name | str | ❌ | Name of the model to be stored in the database. |
cursor | DBcursor | ✓ | Vertica DB cursor. |
tol | float | ✓ | Determines whether the algorithm has reached the specified accuracy result. |
max_iter | int | ✓ | Determines the maximum number of iterations the algorithm performs before achieving the specified accuracy result. |
solver | str | ✓ | The optimizer method to use to train the model.
|
Attributes¶
After the object is created, all parameters become attributes. Additional attributes will be created when fitting the model:
| Name | Type | Description |
|---|---|---|
coef_ | tablesample | Coefficients and their mathematical information (pvalue, std, value...) |
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 |
|---|---|
| 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. |
| plot | Draws the Linear Regression if the number of predictors is equal to 1 or 2. |
| predict | Predicts using the input relation. |
| regression_report | Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...). |
| score | Computes the model score. |
| set_cursor | Sets a new DB cursor. |
| set_params | Sets the parameters of the model. |
| shapExplainer | Creates a shapExplainer for the model. |
| to_sklearn | Converts this Vertica model to an sklearn model. |
Example¶
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from verticapy.learn.linear_model import LinearRegression
model = LinearRegression(name = "public.LR_winequality")
display(model)