asymtree

v0.1.9 suspicious
6.0
Medium Risk

Decision tree classifier with centroid-asymmetry split criterion

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate risk level primarily due to the anonymous authorship and low activity in the git repository, raising concerns about potential supply-chain risks.

  • Anonymous author
  • Low activity in the git repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, suggesting legitimate use.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of secrets.
  • Metadata: The package shows some red flags such as an anonymous author and low activity in the git repository, but there's no clear evidence of malicious intent.

📦 Package Quality Overall: Low (3.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_classifier.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3549 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 16 commits in lcsnxn/asymtree
  • Single author with few commits — possibly a personal or throwaway project

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 asymtree 
Create a data science mini-app that utilizes the 'asymtree' package to classify a dataset of customer churn. The app should allow users to upload their own dataset (CSV format), preprocess the data if necessary, and then train a decision tree model using the centroid-asymmetry split criterion provided by 'asymtree'. Here are the key steps and features:

1. **Data Upload**: Implement a feature allowing users to upload a CSV file containing customer data. Ensure the dataset includes at least one categorical target variable indicating whether a customer has churned or not.
2. **Data Preprocessing**: Automatically handle missing values, encode categorical variables, and scale numerical features as needed. Provide options for users to customize preprocessing steps.
3. **Model Training**: Use the 'asymtree' package to train a decision tree classifier on the preprocessed dataset. Highlight the unique aspect of using centroid-asymmetry for splitting nodes over traditional methods like Gini or entropy.
4. **Visualization**: Offer visual representations of the trained decision tree, including a graphical depiction of the tree structure and feature importance plots.
5. **Prediction and Evaluation**: Allow users to input new customer data and predict whether these customers are likely to churn. Additionally, provide metrics such as accuracy, precision, recall, and F1-score to evaluate the model's performance.
6. **Interactive Dashboard**: Develop a user-friendly dashboard where users can interactively explore different aspects of their dataset and the trained model.
7. **Documentation and Help**: Include comprehensive documentation explaining how each part of the app works, especially focusing on the use of 'asymtree' and why centroid-asymmetry is beneficial in certain scenarios.

💬 Discussion Feed

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