[Solved] 'super' object has no attribute '__sklearn_tags__'

Written by - Aionlinecourse3058 times views

[Solved] 'super' object has no attribute '__sklearn_tags__'

If you're working with scikit-learn and encountered the error:

AttributeError: 'Super' object has no attribute 'sklearn_tags'

You are not the only one! Indeed, this error can sometimes be quite troublesome. Don't worry, we will not leave you all by yourself. This guide will guide you through the mistake and take you through the necessary steps to fix it.

Why Does This Error Occur?

  • The parent class is outdated or lacks the sklearn_tags attribute.
  • There is a mismatch between the custom implementation and the newer scikit-learn API.

Solution 1: Check Your scikit-learn Version

Run the following command to check your scikit-learn version:

pip show scikit-learn

Ensure you’re using a compatible version. If the version is outdated, upgrade to the latest version:

pip install --upgrade scikit-learn

This ensures your environment uses the latest API, reducing compatibility issues.

See also sklearn Issue#30479 and 1.6.1 release notes.


Solution 2: Update Third-Party Dependencies

If the error originates from third-party libraries dependent on scikit-learn, ensure those libraries are updated. Use:

pip list --outdated

Update outdated packages to maintain compatibility:

pip install --upgrade <package_name>

This avoids conflicts between library versions.

Solution 3: Pin Compatible Versions

If upgrading is not feasible, consider pinning the scikit-learn version to match your existing implementation. For example:

pip install scikit-learn==1.1.3

This ensures consistency in your development environment.



Thank you for reading the article. To avoid issues in the future, it is necessary but useful to keep your dependencies up-to-date and follow the standards of your chosen libraries. If you benefitted from this guide, share it with your friends! For more troubleshooting tips, visit our site.


Recommended Projects

Deep Learning Interview Guide

Topic modeling using K-means clustering to group customer reviews

Have you ever thought about the ways one can analyze a review to extract all the misleading or useful information?...

Natural Language Processing
Deep Learning Interview Guide

Medical Image Segmentation With UNET

Have you ever thought about how doctors are so precise in diagnosing any conditions based on medical images? Quite simply,...

Computer Vision
Deep Learning Interview Guide

Build A Book Recommender System With TF-IDF And Clustering(Python)

Have you ever thought about the reasons behind the segregation and recommendation of books with similarities? This project is aimed...

Machine LearningDeep LearningNatural Language Processing
Deep Learning Interview Guide

Automatic Eye Cataract Detection Using YOLOv8

Cataracts are a leading cause of vision impairment worldwide, affecting millions of people every year. Early detection and timely intervention...

Computer Vision
Deep Learning Interview Guide

Crop Disease Detection Using YOLOv8

In this project, we are utilizing AI for a noble objective, which is crop disease detection. Well, you're here if...

Computer Vision
Deep Learning Interview Guide

Vegetable classification with Parallel CNN model

The Vegetable Classification project shows how CNNs can sort vegetables efficiently. As industries like agriculture and food retail grow, automating...

Machine LearningDeep Learning
Deep Learning Interview Guide

Banana Leaf Disease Detection using Vision Transformer model

Banana cultivation is a significant agricultural activity in many tropical and subtropical regions, providing a vital source of income and...

Deep LearningComputer Vision