hybridtffs 0.1.7
Mo ta ngan
Requires Python: >=3.6

# get_features_by_backward_and_tffs

## Description
The `get_features_by_backward_and_tffs` function performs feature selection based on **Backward Selection** combined with **Feature Selection based on Top Frequency (TFFS)**. This function helps identify important features in a dataset by leveraging the **TFFS** algorithm from the [pypi-tffs](https://github.com/VuKieuAnh/pypi-tffs) repository and Backward Selection.

## Parameters

| Parameter         | Data Type         | Description |
|------------------|------------------|--------------------------------------------------------------------------------------|
| `data`          | `pd.DataFrame`    | The input dataset containing features to be selected. The first column should represent the class label for the classification process. |
| `percent_tffs`  | `float`           | The percentage of features to retain after applying **TFFS**. This value should be greater than `percent_backward` to ensure effective selection. |
| `number_run`    | `int`             | The number of times the Random Forest model is built during the TFFS process. |
| `n_estimators`  | `int`             | The number of decision trees in the Random Forest model used to assess feature importance. |
| `percent_backward` | `float`          | The percentage of features to retain after applying Backward Selection. |

## Workflow
1. **Feature Selection based on Top Frequency (TFFS)**:
   - Apply the TFFS algorithm from [pypi-tffs](https://github.com/VuKieuAnh/pypi-tffs) to identify features that frequently appear as important across multiple Random Forest model runs.
   - Retain the top `percent_tffs`% most frequently selected features.

2. **Backward Selection**:
   - Iteratively remove the least important features and evaluate model performance to identify those with the least impact.

3. **Backward Selection**:
   - From the set of features selected in the TFFS step, retain `percent_backward`% of the most impactful features.

## Feature Selection Methods
In addition to **Backward Selection**, this package includes multiple classical feature selection methods combined with TFFS:
- **Forward Selection** (`get_features_by_forward_and_tffs`)
- **Recursive Feature Elimination (RFE)** (`get_features_by_recursive_and_tffs`)
- **Pearson Correlation (P.C.)** (`get_features_by_pc_and_tffs`)
- **Mutual Information (M.I.)** (`get_features_by_mi_and_tffs`)
- **Fish Score** (`get_features_by_fs_and_tffs`)
- **Lasso Regression** (`get_features_by_lasso_and_tffs`)

## Example Usage
```python
import pandas as pd
from hybridtffs import get_features_by_backward_and_tffs

# Create a sample DataFrame
data = pd.DataFrame({
    'class': [0, 1, 0, 1, 2, 0, 1, 2, 0, 1],
    'feature_1': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
    'feature_2': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature_3': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
    'feature_4': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature_5': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
    'feature_6': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature_7': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
    'feature_8': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
    'feature_9': [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
})

# Run the function
selected_features = get_features_by_backward_and_tffs(
    data,
    percent_tffs=50,
    number_run=10,
    n_estimators=100,
    percent_backward=30
)

print("Selected features:", selected_features)
```

## Author
**Vu Thi Kieu Anh** 

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