> For the complete documentation index, see [llms.txt](https://seattle-university.gitbook.io/sdgne/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://seattle-university.gitbook.io/sdgne/smote-variants/sdd-smote-with-incremental-boosting.md).

# SDD SMOTE with incremental boosting

This SMOTE variant, known as SDD SMOTE with incremental boosting, uses the same data generation logic as in SDD SMOTE, but in addition to it, it uses incremental boosting as an ensemble learning approach to improve data quality and accuracy. 

```python
from sdgne.datagenerator.smote import SDD_BoostCC

minority_column_label = 'class'
minority_class_label = 0

synthesizer = SDD_BoostCC(dataset,minority_column_label,minority_class_label)

synthesize_data = synthesizer.data_generator()
```

## Importing SMOTE

```python
from sdgne.datagenerator.smote import SDD_BoostCC
```

***

## Creating a synthesizer

```python
synthesizer = SDD_BoostCC(dataset, minority_column_label, minority_class_label)
```

### Parameters

<table data-header-hidden data-full-width="false"><thead><tr><th width="216.66666666666669">variable</th><th width="140">type</th><th>datatype</th><th>info</th></tr></thead><tbody><tr><td>dataset</td><td>required</td><td>pd.Dataframe</td><td>Represents a pandas data frame containing both, the original minority and the original majority data.</td></tr><tr><td>minority_column_label</td><td>required</td><td>string</td><td>Represents the column label. Eg. 'class', 'output'</td></tr><tr><td>minority_class_label</td><td>required</td><td>string</td><td>Represents the minority class label. Eg. '1', '0'</td></tr></tbody></table>

### Returns

An instance of class SDD\_BoostCC.

## Synthetic Data Generation

### data\_generator()

The data generator function generates synthetic data using the synthesizer. It has an option parameter  `num_to_synthesize.`

* If `num_to_synthesize` is not defined, data\_generator by default generates the \`n\` number of synthetic data such that the majority data and minority datasets get balanced.
* If `num_to_synthesize` is defined and the dataset is already balanced, data\_generator generates 2 \* Number of original minority data.
* If `num_to_synthesize` is defined and the dataset is not balanced, it generates synthetic data equal to the value passed.

```python
synthesized_data = synthesizer.data_generator(no_of_syntetic_data)
```

### Parameters

<table data-header-hidden><thead><tr><th width="222">variable</th><th width="154">type</th><th width="107">datatype</th><th>info</th></tr></thead><tbody><tr><td><code>num_to_synthesize</code></td><td>optional (default: None)</td><td>integer</td><td>Represents the number of synthetic data to be generated.</td></tr></tbody></table>

### Returns

A pandas data frame that combines original data and synthetic data.

### Usage

`synthesizer.data_generator()`

`synthesizer.data_generator(num_to_synthesize=100)`