https://github.com/senapedev/iris-binary-classification
Single layer perceptron for binary classification of two iris species.
https://github.com/senapedev/iris-binary-classification
binary-classification iris-dataset machine-learning
Last synced: 9 days ago
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Single layer perceptron for binary classification of two iris species.
- Host: GitHub
- URL: https://github.com/senapedev/iris-binary-classification
- Owner: SenapeDev
- License: mit
- Created: 2025-01-06T11:39:28.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-06T14:23:38.000Z (over 1 year ago)
- Last Synced: 2025-11-17T20:31:58.288Z (7 months ago)
- Topics: binary-classification, iris-dataset, machine-learning
- Language: Python
- Homepage:
- Size: 316 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Iris binary classification model
This project implements a simple binary classification model using logistic regression to classify two species of the Iris dataset: **Iris-setosa** and **Iris-versicolor**. The model uses the first two features of the dataset (*sepal length* and *sepal width*) and is trained using gradient descent.
## Features
- Binary classification of two Iris species.
- Visualizes the decision boundary during training and testing.
- Evaluates model accuracy on the test set.
- Fully modular code for ease of reuse and modification.
## Dataset
The code uses the [Iris dataset](https://archive.ics.uci.edu/dataset/53/iris), which contains measurements of 150 flowers from three species:
- **Iris-setosa**
- **Iris-versicolor**
- **Iris-virginica**
### Data used in this project
For this binary classification task:
- **Iris-setosa** is labeled as `0`.
- **Iris-versicolor** is labeled as `1`.
- The first two features are used:
- Sepal length (in cm)
- Sepal width (in cm)
## Dependencies
The following Python libraries are required to run the code:
- `numpy`
- `pandas`
- `matplotlib`
### Installation
```python
pip install numpy pandas matplotlib
```
## Code analysis
### Data loading and preprocessing
The dataset is loaded from the `iris.data` file. Only the first two features and two classes are used.
- `load_data(file_name)`
Splits the dataset into:
- **Training set**: 80% of the data (40 samples of each class).
- **Test set**: 20% of the data (10 samples of each class).
### Model Initialization
The model is initialized with random weights and a bias.
- `initialize_parameters(features)`
- Random values for weights and bias are generated within the range $[-0.5, 0.5]$.
- A weight is generated for each feature.
### Model Training
The model is trained using gradient descent for a specified number of epochs. The training process visualizes the decision boundary at 10 evenly spaced epochs.
- `train_model(x_train, y_train, weights, bias, epochs, eta)`
### **Activation**
The model's activation is calculated as:
>$$y = \sigma(w \cdot x + b)$$
Where:
- $\sigma(z) = \frac{1}{1 + e^{-z}}$ is the sigmoid function.
- $w$: Weight vector.
- $x$: Input features.
- $b$: Bias.
### **Gradient**
The gradient of the error with respect to the prediction is given by:
>$$\text{gradient} = (y - y_{\text{expected}}) \cdot y \cdot (1 - y)$$
Where:
- $y_{\text{expected}}$: Expected value (target).
- $y$: Predicted output.
#### Weight Update
Weights are updated according to the gradient rule:
>$$w \leftarrow w - \eta \cdot \frac{1}{N} \cdot (x^T \cdot \text{gradient})$$
Where:
- $\eta$: Learning rate.
- $N$: Number of samples in the batch.
- $x^T$: Transpose of the input matrix.
### **4. Bias Update**
The bias is updated as:
>$$b \leftarrow b - \eta \cdot \frac{1}{N} \cdot \sum \text{gradient}$$
### Evaluation
The model is evaluated on the test set:
- `evaluate_model(x_test, y_test, weights, bias)`
### Visualization
The decision boundary and the predictions on the test set are visualized:
- `visualize_test_predictions(x_test, y_pred_test, weights, bias)`
## How to run
### Clone the repository
```bash
git clone https://github.com/SenapeDev/iris-binary-classification
```
### Download the dataset
Place the `iris.data` file in the same directory as the script. [Download](https://archive.ics.uci.edu/dataset/53/iris)
### Run the script
```bash
python3 neural_network.py
```
## Output
```
Dataset Iris loaded:
0 1 2 3 4
0 5.1 3.5 1.4 0.2 Iris-setosa
1 4.9 3.0 1.4 0.2 Iris-setosa
2 4.7 3.2 1.3 0.2 Iris-setosa
3 4.6 3.1 1.5 0.2 Iris-setosa
4 5.0 3.6 1.4 0.2 Iris-setosa
Accuracy on test set: 95.00%
```
### Screenshots
#### Training
During training, the decision boundary evolves to separate the two classes.
#### Test
After training, the test set is visualized with predictions and the decision boundary.
