# Command Line Basics¶

This tutorial introduces Vowpal Wabbit command line basics with a quick introduction to training and testing your model with Vowpal Wabbit. 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.

Prerequisites

To install Vowpal Wabbit see Get Started.

## Training scenario and dataset¶

For this tutorial scenario, we want to use Vowpal Wabbit to help us predict whether or not a house will require a new roof in the next 10 years.

First, create a file `train.txt` and copy the following dataset:

```0 | price:.23 sqft:.25 age:.05 2006
1 | price:.18 sqft:.15 age:.35 1976
0 | price:.53 sqft:.32 age:.87 1924
```

Note: If the format of this sample dataset looks unfamiliar and you want more details see the Vowpal Wabbit Linear Regression Tutorial for information on input format and feature hashing techniques.

## Train a model¶

Next, we train a model, and save it to a file:

```vw -d train.txt -f model.vw
```

This tells Vowpal Wabbit to:

• Use the `-d` data file `train.txt`.

• Write the `-f` final model to `model.vw`.

With Vowpal Wabbit, the output includes more than a few statistics and statuses. The Linear Regression Tutorial and Contextual Bandit Reinforcement Learning Tutorial covers this format in more detail:

Output:

```final_regressor = model.vw
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
0.000000 0.000000            1            1.0   0.0000   0.0000        5
0.500000 1.000000            2            2.0   1.0000   0.0000        5

finished run
number of examples = 3
weighted example sum = 3.000000s
weighted label sum = 1.000000
average loss = 0.666667
best constant = 0.333333
best constant's loss = 0.222222
total feature number = 15
```

## Test a model¶

Now, create a file called `test.txt` and copy this data:

```| price:.46 sqft:.4 age:.10 1924
```

We get a prediction by loading the model and supplying our test data:

```vw -d test.txt -i model.vw -p predictions.txt
```

This tells Vowpal Wabbit to:

• Use the `-d` data file `test.txt`.

• Use the `-i` input model `model.vw`.

• Write `-p` predictions to `predictions.txt`.

Output:

```predictions = predictions.txt
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
n.a.     n.a.            1            1.0  unknown   0.0000        5

finished run
number of examples = 1
weighted example sum = 1.000000
weighted label sum = 0.000000
average loss = n.a.
```

`cat predictions.txt` shows:

```0
```

### Vowpal Wabbit results¶

The model predicted a value of 0. This result means our house will not need a new roof in the next 10 years (based on just three examples we used in our training dataset).

## More to explore¶

• See Python tutorial for a quick introduction to the basics of training and testing your model.

• To learn more about how to approach a contextual bandits problem using Vowpal Wabbit — including how to work with different contextual bandits approaches, how to format data, and understand the results — see the Contextual Bandit Reinforcement Learning Tutorial.

• For more on the contextual bandits approach to reinforcement learning, including a content personalization scenario, see the Contextual Bandit Simulation Tutorial.

• See the Linear Regression Tutorial for a different look at the roof replacement problem and learn more about Vowpal Wabbit’s format and understanding the results.