This tutorial is a quick introduction to training and testing your model with Vowpal Wabbit using Python. We explore passing some data to Vowpal Wabbit to learn a model and get a prediction.
For more advanced Vowpal Wabbit tutorials, including how to format data and understand results, see Tutorials.
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To install Vowpal Wabbit see [Get Started](https://vowpalwabbit.org/start.html).
First, import the Vowpal Wabbit Python package for this tutorial:
import vowpalwabbit
Next, we create an instance of Vowpal Wabbit, and pass the quiet=True
option to avoid diagnostic information output to stdout
location:
model = vowpalwabbit.Workspace(quiet=True)
For this tutorial scenario, we want Vowpal Wabbit to help us predict whether or not our house will require a new roof in the next 10 years.
To create some examples, we use the Vowpal Wabbit text format and then learn on them:
train_examples = [
"0 | price:.23 sqft:.25 age:.05 2006",
"1 | price:.18 sqft:.15 age:.35 1976",
"0 | price:.53 sqft:.32 age:.87 1924",
]
for example in train_examples:
model.learn(example)
Note: For more details on Vowpal Wabbit input format and feature hashing techniques see the Linear Regression Tutorial.
Now, we create a test_example
to use for prediction:
test_example = "| price:.46 sqft:.4 age:.10 1924"
prediction = model.predict(test_example)
print(prediction)
The model predicted a value of 0. According to our learning model, our house will not need a new roof in the next 10 years (at least that is the result from just three examples we used in our training dataset).