Welcome to Deep Evaluation, a Jupyter Notebook designed to guide you through a structured and insightful evaluation of machine learning models, including deep learning components. This notebook is tailored for data scientists and ML engineers looking to perform rigorous evaluation, diagnostics, and improvements in their models.
- Introduction
- Setup & Installation
- Notebook Structure
- Sample Outputs
- How to Use
- Dependencies
- Contributing
- License
This notebook performs deep evaluations of machine learning models using:
- Model accuracy, precision, recall, F1-score
- Confusion matrix analysis
- ROC-AUC and PR curves
- Misclassified examples analysis
- Feature importance insights (if applicable)
🛠 Goal: Help you identify model strengths and weaknesses and suggest actionable improvements.
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Clone the repository or download the notebook:
git clone https://github.com/your-username/deep-evaluation.git
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Install required libraries:
pip install -r requirements.txt
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Open the notebook:
jupyter notebook Deep_Evaluation.ipynb
| Section | Description |
|---|---|
| 📌 Introduction | Overview and goals |
| 🧹 Data Preprocessing | Cleans and prepares data |
| 🤖 Model Loading | Load pre-trained or trained model |
| 📊 Evaluation Metrics | Outputs metrics and visualizations |
| 🔍 Error Analysis | Visualizes errors, confusion matrix, etc. |
Here are some sample visualizations this notebook generates:
- Make sure your dataset and trained model are available in the required format.
- Update paths in the notebook under the Data Loading and Model Loading sections.
- Run the notebook step-by-step.
- Interpret results and identify model issues or improvements.
- Python 3.8+
- scikit-learn
- matplotlib
- seaborn
- pandas
- numpy
- Jupyter Notebook
See requirements.txt for full list.
We welcome contributions! Feel free to:
- Open issues
- Create pull requests
- Suggest improvements
Distributed under the MIT License. See LICENSE for more information.
👨💻 Made with ❤️ by [Sumit Kumar]