---
title: "Classification modeling"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{classification}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---




For showing classification `SSLR` models, we will use *Wine* dataset with 20% labeled data:

```{r eval=FALSE}

library(SSLR)
library(tidymodels)
library(caret)
```

```{r include=FALSE}
knitr::opts_chunk$set(
  digits = 3,
  collapse = TRUE,
  comment = "#>"
)
options(digits = 3)

library(SSLR)
library(tidymodels)
library(caret)
```

```{r wine, results="hide"}
data(wine)

set.seed(1)

#Train and test data
train.index <- createDataPartition(wine$Wine, p = .7, list = FALSE)
train <- wine[ train.index,]
test  <- wine[-train.index,]

cls <- which(colnames(wine) == "Wine")

# 20 % LABELED
labeled.index <- createDataPartition(wine$Wine, p = .2, list = FALSE)
train[-labeled.index,cls] <- NA
```

We have multiple models for solving semi-supervised learning problems of classification. You can read [`Model List`](../../docs/articles/models.html) section


For example, we train with Decision Tree:

```{r fit, results="hide"}
m <- SSLRDecisionTree(min_samples_split = round(length(labeled.index) * 0.25),
                      w = 0.3) %>% fit(Wine ~ ., data = train)
```

Now we predict with class (tibble) and prob (tibble:)

```{r testing}
test_results <- 
    test %>%
    select(Wine) %>%
    as_tibble() %>%
    mutate(
        dt_class = predict(m, test) %>% 
            pull(.pred_class)
    )

test_results

```
Now we can use metrics from `yardstick` package:

```{r metrics}
test_results %>% accuracy(truth = Wine, dt_class)

test_results %>% conf_mat(truth = Wine, dt_class)

#Using multiple metrics

multi_metric <- metric_set(accuracy, kap, sens, spec, f_meas )

test_results %>% multi_metric(truth = Wine, estimate = dt_class)
```

In classification models we can use *raw* type of predict for getting labels in factor:

```{r metrics_raw}
predict(m,test,"raw")
```


We can even use probability predictions in the Decision Tree model:


```{r metrics_prob}
predict(m,test,"prob")
```