
VerticaPy
Example: Methods in a Binary Classification Model¶
In this example, we use the 'Titanic' dataset to demonstrate the methods available to binary classification models.
from verticapy.datasets import load_titanic
titanic = load_titanic()
display(titanic)
Let's create a logistic regression to predict the survival of the passengers. We'll use the age and the fare as predictors.
from verticapy.learn.linear_model import LogisticRegression
model = LogisticRegression("public.LR_titanic")
model.fit("public.titanic", ["age", "fare"], "survived")
Fitting the model creates new model attributes, making methods easier to use.
model.X
model.y
model.input_relation
model.test_relation
Since we didn't write a test relation when fitting the model, the model will use the training relation as the test relation.
Let's compute the accuracy of the model.
model.score(method = "accuracy")
The 'score' method uses the 'y' attribute and the model prediction in the 'test_relation' to compute the accuracy of the model. You can change these attributes at any time to deploy the models on different columns.
Models have many useful attributes. For example, the 'coef_' attribute gives us the p-value of the model.
model.coef_
You can view other attributes using the 'get_attr' method.
model.get_attr()
PRC, ROC or lift charts can help you visualize your model.
model.roc_curve()
model.prc_curve()
model.lift_chart()
Let's look at the SQL query for our model.
display(model.deploySQL())
You can evaluate the quality of your model with the 'report' method.
model.report()
You can also add the prediction to your vDataFrame.
model.predict(titanic, name = "pred_survived")