decisioncanvas 1.0.1
Easy decision boundary visualization for classifiers
License: MIT
Requires Python: >=3.7
Keywords: visualization, classifier, decision boundary, matplotlib, scikit-learn

# decisioncanvas

A Python library for visualizing machine learning classifier decision boundaries with automatic dimensionality reduction and standardization.

## Overview

decisioncanvas provides a simple, reliable way to create publication-quality visualizations of how classification models separate data. The library automatically handles high-dimensional data through PCA dimensionality reduction and ensures consistent scaling through built-in standardization.

## Key Features

- **Simple API**: Single function call for complete visualization
- **High-dimensional support**: Automatic PCA with explained variance reporting  
- **Scikit-learn compatibility**: Works with any scikit-learn classifier
- **Multi-class support**: Handles binary and multi-class classification
- **Built-in preprocessing**: Automatic feature standardization
- **Customizable visualization**: Configurable grid resolution, colors, and styling
- **Educational focus**: Ideal for learning and teaching machine learning concepts

## Installation

```bash
pip install decisioncanvas
```

## Quick Start

```python
from decisioncanvas import plot_decision_boundary
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

# Load data and create model
X, y = load_iris(return_X_y=True)
model = LogisticRegression(max_iter=300)

# Visualize decision boundary
plot_decision_boundary(model, X, y)
```

## Usage Examples

### Basic Classification

```python
from decisioncanvas import plot_decision_boundary
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Prepare data
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(
    data.data, data.target, test_size=0.3, random_state=42
)

# Train model
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

# Visualize with custom labels
plot_decision_boundary(
    model,
    X_train,
    y_train,
    class_names=data.target_names,
    title='Iris Classification Decision Boundary'
)
```

### Advanced Customization

```python
plot_decision_boundary(
    model,
    X,
    y,
    grid_resolution=400,
    alpha=0.4,
    padding=1.5,
    title='Custom Decision Boundary Visualization',
    figsize=(10, 8)
)
```

## API Reference

### plot_decision_boundary

```python
plot_decision_boundary(
    model, X, y,
    class_names=None,
    standardize=True,
    pca_components=2,
    grid_resolution=300,
    padding=1.0,
    cmap_light=None,
    cmap_bold=None,
    alpha=0.3,
    title="Decision Boundary",
    fit_model=True,
    figsize=(8, 6),
    random_state=None
)
```

#### Parameters

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `model` | estimator | Scikit-learn compatible classifier | Required |
| `X` | array-like | Feature matrix (n_samples, n_features) | Required |
| `y` | array-like | Target labels (n_samples,) | Required |
| `class_names` | list or dict | Custom class labels for legend | None |
| `standardize` | bool | Apply feature standardization | True |
| `pca_components` | int | Number of PCA components for dimensionality reduction | 2 |
| `grid_resolution` | int | Resolution of decision boundary grid | 300 |
| `padding` | float | Padding around data points in plot | 1.0 |
| `cmap_light` | colormap | Colormap for decision regions | None (auto) |
| `cmap_bold` | list | Colors for data points | None (auto) |
| `alpha` | float | Transparency of decision regions | 0.3 |
| `title` | str | Plot title | "Decision Boundary" |
| `fit_model` | bool | Whether to fit the model on provided data | True |
| `figsize` | tuple | Figure size (width, height) | (8, 6) |
| `random_state` | int | Random state for PCA reproducibility | None |

## Use Cases

- **Model evaluation**: Visualize how different classifiers separate your data
- **Educational purposes**: Demonstrate classification concepts in tutorials
- **Research presentations**: Create publication-ready decision boundary plots
- **Debugging**: Identify potential overfitting or data separation issues

## Requirements

- Python >= 3.7
- numpy >= 1.18.0
- scikit-learn >= 0.24.0
- matplotlib >= 3.2.0

## Contributing

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Authors

- **Krunal Wankhade** - krunal.wankahde1810@gmail.com
- **Parimal Kalpande** - kalpandeparimal60@gmail.com

## Links

- **PyPI**: https://pypi.org/project/decisioncanvas/
- **GitHub**: https://github.com/KrunalWankhade9021/Decesion-Canvas