This package includes a collection of methods to create models for semi-supervised learning (e.g. fitting the model, making predictions, etc), with a fairly intuitive interface that is easy to use.

In `Model list section`

you can see the list of different classification and regression models.

Current packages to do semi-supervised learning do not use an intuitive interface. In this package, trying to use semi-supervised learning in an easy and intuitive way.

`SSLR`

tries to solve this by providing an interface to use different models, mainly using the parsnip model interface to make the use of this package easier.

`SSLR`

connects with parsnip to create different models without using too many arguments in the fit functions.

In addition, it uses other packages such as `RSSL`

to use the same interface in an easy way.

For example, to use different ones like `RSSL`

. It has a different interface. Thanks to SSLR you can use different options to use its fit functions.

To fit the model (for example SelfTraining), you must:

- Have a defined model using parsnip
- Use your
*parameters*or using by default - Call
`fit`

with formula,`fit_xy`

with x and y, or`fit_x_u`

with x and unlabeled data. See`Model fitting section`

.

For example, with `fit`

function:

```
rf <- rand_forest(trees = 100, mode = "classification") %>%
set_engine("randomForest")
m <- selfTraining(learner = rf) %>% fit(Wine ~ ., data = train)
```

Or with `fit_xy`

function:

```
rf <- rand_forest(trees = 100, mode = "classification") %>%
set_engine("randomForest")
m <- selfTraining(learner = rf) %>% fit_xy(x = train[,-cls], y = train$Wine)
```

This uses the `parsnip`

package that has an intuitive interface to create a Random Forest model and this can be used in the `SSLR`

package in a simple way.