https://github.com/samridhisainii/yellowbricks
Yellow Brick Practice
https://github.com/samridhisainii/yellowbricks
python3 yellowbrick
Last synced: 2 months ago
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Yellow Brick Practice
- Host: GitHub
- URL: https://github.com/samridhisainii/yellowbricks
- Owner: SAMRIDHISAINII
- Created: 2023-11-16T11:39:17.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-07T07:10:26.000Z (over 2 years ago)
- Last Synced: 2025-01-28T09:49:05.317Z (over 1 year ago)
- Topics: python3, yellowbrick
- Language: Jupyter Notebook
- Homepage:
- Size: 1.32 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Yellow Bricks: Machine Learning Visualization Library
Yellow Bricks is a powerful machine learning visualization library built to enhance your data science workflow by providing insightful visualizations and tools to better understand, diagnose, and improve your machine learning models. Whether you are a beginner or an experienced practitioner, Yellow Bricks aims to make the process of model interpretation and evaluation more accessible and efficient.
## Why Yellow Bricks?
- **Interpretability**: Yellow Bricks offers a suite of visualizations to help you interpret the behavior of your models, making it easier to understand their decisions.
- **Model Evaluation**: Evaluate your machine learning models with a variety of visual tools, including learning curves, feature importance plots, and more, to assess performance and identify areas for improvement.
- **Debugging**: Diagnose common issues and pitfalls in your models through visualization, allowing you to debug and optimize your machine learning pipelines effectively.
- **Educational**: Yellow Bricks is designed with education in mind, providing a valuable resource for learning machine learning concepts and techniques through visual aids and examples.
## Getting Started
1. Install Yellow Bricks using pip:
```bash
pip install yellowbricks
```
2. Import Yellow Bricks in your Python script or Jupyter notebook:
```python
import yellowbrick as yb
```
3. Explore the various visualizations and tools available in Yellow Bricks. Check the [documentation](https://yellowbricks.readthedocs.io/) for detailed information and examples.
## Examples
Here are a few examples showcasing the capabilities of Yellow Bricks:
### Learning Curve
```python
from yellowbrick.model_selection import LearningCurve
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
# Load your dataset and split into training and testing sets
X, y = load_your_dataset()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create a model (e.g., Logistic Regression)
model = LogisticRegression()
# Visualize the learning curve
visualizer = LearningCurve(model, scoring='accuracy')
visualizer.fit(X_train, y_train)
visualizer.show()
```
## Feature Importance
```python
from yellowbrick.model_selection import FeatureImportances
from sklearn.ensemble import RandomForestClassifier
# Load your dataset
X, y = load_your_dataset()
# Create a model (e.g., Random Forest)
model = RandomForestClassifier()
# Visualize feature importances
visualizer = FeatureImportances(model)
visualizer.fit(X, y)
visualizer.show()
```
### Supported Stacks
Yellow Bricks is compatible with a variety of machine learning stacks, including but not limited to:
- [Scikit-Learn](https://scikit-learn.org/)
- [XGBoost](https://xgboost.readthedocs.io/)
- [LightGBM](https://lightgbm.readthedocs.io/)
- [TensorFlow](https://www.tensorflow.org/)
- [PyTorch](https://pytorch.org/)
Check the [documentation](https://yellowbricks.readthedocs.io/) for the complete list of supported libraries and frameworks.
Feel free to add or modify the links based on the official documentation or relevant resources for each library or framework.