# 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
Reading datafile = train.txt
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
Reading datafile = test.txt
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 tVowpal 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.