STree

v1.4.2 safe
3.0
Low Risk

Oblique decision tree with svm nodes.

🤖 AI Analysis

Final verdict: SAFE

The package STree v1.4.2 appears to be safe with minimal risks identified. It shows no signs of malicious activity such as network calls, shell execution, or credential harvesting.

  • No network calls detected.
  • Maintainer's account is new or inactive, raising minor concerns.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires online functionality.
  • Shell: No shell execution detected, indicating no immediate risk of command injection or privilege escalation.
  • Obfuscation: No obfuscation patterns detected, suggesting legitimate code practices.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of secrets.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, which could indicate potential issues but does not definitively suggest malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: alu.uclm.es>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Doctorado-ML/STree appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with STree 
Develop a predictive analytics tool using Python's 'STree' package, which specializes in oblique decision trees with SVM nodes. This tool will enable users to classify datasets based on complex, non-linear boundaries, making it particularly useful for tasks where traditional decision trees might fall short due to their reliance on axis-aligned splits. Your goal is to create a user-friendly interface where users can upload their dataset, specify the target variable, and receive predictions along with visualizations of the decision tree structure. Here’s a detailed plan:

1. **Setup**: Begin by installing the necessary packages including 'STree'. Ensure you have a robust environment set up for data manipulation and visualization.
2. **Data Input Module**: Create a module that allows users to upload CSV files. Validate the data and ensure it meets the requirements for the 'STree' package.
3. **Model Training**: Implement functionality to train an oblique decision tree model using the uploaded dataset. Use 'STree' to handle the complexity of the decision-making process.
4. **Prediction Engine**: Develop a prediction engine that uses the trained model to classify new data points. Provide options for users to input new data and receive classifications.
5. **Visualization**: Utilize libraries like Matplotlib or Plotly to visualize the decision tree structure. Highlight the oblique splits made by the 'STree' package to illustrate its unique capabilities.
6. **User Interface**: Design a simple, intuitive web interface using Flask or Django. Allow users to navigate through different functionalities seamlessly.
7. **Evaluation Metrics**: Include a feature that calculates common evaluation metrics such as accuracy, precision, recall, and F1-score to assess the performance of the model.
8. **Documentation and User Guide**: Prepare comprehensive documentation and a user guide to help other developers understand how to integrate and extend your application.

This project aims to showcase the power of 'STree' in handling complex classification tasks and provide a practical tool for data scientists and analysts.