‘super’ object has no attribute ‘sklearn_tags’ Error in Python

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If you’re a Python developer working with machine learning libraries like scikit-learn, you might have stumbled across the error: “‘super’ object has no attribute ‘sklearn_tags'”. This cryptic message can be frustrating, especially if you’re new to Python or object-oriented programming (OOP). But don’t worry! In this comprehensive, beginner-friendly guide, we’ll break down why this Python super error occurs, how to fix it, and how to prevent it in the future.

What Does the Error Message Mean?

Let’s begin by unpacking the error. When you see “‘super’ object has no attribute ‘sklearn_tags'”, Python is telling you that the code tried to access an attribute called __sklearn_tags__ on a super object, but it doesn’t exist. To understand why, let’s explore:

  • What is the super() function in Python?
    The super() function is used in class inheritance to call methods or access attributes from a parent class. For example, if you’re creating a custom class that inherits from a scikit-learn class, super() helps you reuse the parent’s functionality.
  • What is __sklearn_tags__?
    This is an internal attribute used by scikit-learn to store metadata about estimators (e.g., classifiers, regressors). It’s not something you typically interact with directly, but it’s accessed by scikit-learn’s internal checks.
  • Why is this error happening?
    The error suggests that super() is trying to access __sklearn_tags__ from a parent class, but either the parent class doesn’t have this attribute, or super() is being used incorrectly.

Let’s dive deeper to uncover the root causes.

Why Does the ‘super’ object has no attribute ‘sklearn_tags’ Error Occur?

To figure out why this Python super error happens, let’s consider the common scenarios where it might appear. Think about your code: Are you working with scikit-learn? Are you creating a custom class? Here are the likely causes:

  • Incorrect Use of super(): You might be calling super() in a way that points to the wrong parent class or doesn’t initialize the parent properly.
  • Inheritance from a Non-scikit-learn Class: If your custom class inherits from a base class that isn’t a scikit-learn estimator, the parent won’t have __sklearn_tags__.
  • Outdated scikit-learn Version: Older versions of scikit-learn might handle __sklearn_tags__ differently, causing compatibility issues.
  • Improper Class Initialization: If you override the parent class’s __init__ method without calling super().__init__(), the parent’s attributes (like __sklearn_tags__) might not be set up.
  • Mixing scikit-learn with Custom Logic: You might be combining scikit-learn’s base classes (e.g., BaseEstimator) with custom code that breaks its internal structure.

Example Scenario

Imagine you’re building a custom scikit-learn estimator:

from sklearn.base import BaseEstimator

class MyCustomClassifier(BaseEstimator):
    def __init__(self):
        super().__init__()

If this code triggers the error, it’s likely because super() is accessing BaseEstimator, but something in the inheritance chain or initialization is misconfigured. Let’s explore how to diagnose this.

How Can You Diagnose the Error?

To fix the sklearn tags error, you first need to pinpoint where it’s coming from. Ask yourself:

  • Where is the error occurring?
    Check the stack trace in your error message. It’ll show the line of code causing the issue. Is it in your custom class, a scikit-learn method, or a third-party library?
  • What class are you inheriting from?
    If you’re extending a scikit-learn class, ensure it’s the right one (e.g., BaseEstimator, ClassifierMixin, or RegressorMixin). Not all scikit-learn classes have __sklearn_tags__.
  • Are you using super() correctly?
    In Python 3, you can use super() without arguments, but you need to ensure it’s called within a class that properly inherits from a parent. For example: super().__init__() # Correct in Python 3
  • What’s your scikit-learn version?
    Run pip show scikit-learn to check your version. If it’s outdated (e.g., pre-1.0), updating might resolve the issue.

Let’s move on to solutions based on these insights.

How to Fix the ‘super’ object has no attribute ‘sklearn_tags’ Error

Now that we’ve explored the causes, let’s look at practical solutions. Here are step-by-step fixes for the super object has no attribute sklearn_tags error:

1. Verify Correct Use of super()

Ensure you’re using super() correctly in your class. In Python 3, the syntax is simple:

class MyCustomClassifier(BaseEstimator):
    def __init__(self):
        super().__init__()  # Calls BaseEstimator's __init__

If you’re using Python 2 or explicitly specifying the class, it looks like this:

super(MyCustomClassifier, self).__init__()

Question: Are you passing the correct class and instance to super()? Double-check your class definition and inheritance.

2. Check Your Parent Class

Make sure your custom class inherits from the appropriate scikit-learn base class. For example, BaseEstimator is a common parent for scikit-learn estimators. If you inherit from a class that doesn’t define __sklearn_tags__, you’ll get the error.

Example Fix:

from sklearn.base import BaseEstimator, ClassifierMixin

class MyCustomClassifier(BaseEstimator, ClassifierMixin):
    def __init__(self):
        super().__init__()

Question: Is your parent class a scikit-learn estimator (e.g., BaseEstimator, ClassifierMixin)? If not, consider switching to the correct base class.

3. Update scikit-learn

The __sklearn_tags__ attribute was introduced in newer versions of scikit-learn to handle estimator metadata. If you’re using an older version, update it:

pip install --upgrade scikit-learn

As of July 27, 2025, the latest version is likely 1.5.x or higher. Check your version:

import sklearn
print(sklearn.__version__)

Question: Could an outdated library be causing the issue? Try updating and re-running your code.

4. Ensure Proper Initialization

If you override __init__, always call the parent’s __init__ to set up its attributes, including __sklearn_tags__.

Incorrect:

class MyCustomClassifier(BaseEstimator):
    def __init__(self):
        # No super().__init__() call
        pass

Correct:

class MyCustomClassifier(BaseEstimator):
    def __init__(self):
        super().__init__()  # Ensures BaseEstimator's attributes are set

Question: Are you skipping the parent’s initialization? Add super().__init__() to your __init__ method.

5. Implement __sklearn_tags__ (Advanced)

If you’re creating a custom estimator and need __sklearn_tags__, you can define it manually. This attribute is a dictionary that specifies metadata about your estimator, like whether it supports certain features (e.g., predict_proba).

Example:

from sklearn.base import BaseEstimator

class MyCustomClassifier(BaseEstimator):
    def __init__(self):
        super().__init__()

    def _get_tags(self):
        return {
            "allow_nan": False,
            "requires_y": True,
            "binary_only": False,
            # Add other relevant tags
        }

Note: As of scikit-learn 1.0+, _get_tags is the preferred way to define tags, but __sklearn_tags__ might still be accessed internally in some cases.

Question: Do you need to define custom tags for your estimator? Check scikit-learn’s documentation for the required tags.

6. Debug with a Minimal Example

Create a minimal, reproducible example to isolate the issue. For example:

from sklearn.base import BaseEstimator

class MyCustomClassifier(BaseEstimator):
    def __init__(self):
        super().__init__()

    def fit(self, X, y):
        return self

    def predict(self, X):
        return [0] * len(X)

# Test the class
model = MyCustomClassifier()
model.fit([[1, 2], [3, 4]], [0, 1])

If this works without errors, the issue might be in your specific implementation or dependencies.

Question: Can you reproduce the error with a small code snippet? This helps narrow down the problem.

Common Scenarios Where the Error Occurs

Let’s look at real-world scenarios where the sklearn tags error might pop up:

  • Custom Pipelines: If you’re creating a custom transformer or estimator for a scikit-learn pipeline, ensure all classes inherit from BaseEstimator or other appropriate mixins (e.g., TransformerMixin).
  • Third-Party Libraries: Some libraries extend scikit-learn’s functionality and might misuse super() or expect a specific version of scikit-learn.
  • Complex Inheritance: If your class inherits from multiple parents, Python’s method resolution order (MRO) might cause super() to access the wrong class.

Example with a Pipeline:

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline

class MyTransformer(BaseEstimator, TransformerMixin):
    def __init__(self):
        super().__init__()

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return X

pipeline = Pipeline([('custom', MyTransformer())])

If this triggers the error, check the parent classes and initialization.

Best Practices to Avoid the Error

To prevent the Python inheritance issue in the future, follow these best practices:

  • Always Call super().__init__(): Ensure parent class initialization is included in your __init__ method.
  • Use Correct Base Classes: Stick to scikit-learn’s BaseEstimator, ClassifierMixin, or TransformerMixin for custom estimators.
  • Keep Libraries Updated: Regularly update scikit-learn and other dependencies.
  • Read Documentation: Check scikit-learn’s developer guide for custom estimators.
  • Test Incrementally: Build and test your custom class step by step to catch errors early.

Tools for Debugging

ToolPurposeCost
Python Debugger (pdb)Step through code to find issuesFree
VS CodeDebug Python with breakpointsFree
PyCharmAdvanced debugging and lintingFree/Paid
Stack OverflowSearch for similar errorsFree

FAQs About the ‘super’ object has no attribute ‘sklearn_tags’ Error

Why does scikit-learn need __sklearn_tags__?

It’s an internal attribute used to store metadata about estimators, like whether they support missing values or require labels. It helps scikit-learn validate and optimize models.

Can I ignore this error?

No, it usually indicates a problem with your class setup or library compatibility. Fixing it ensures your code works as expected.

Is this error specific to scikit-learn?

Yes, __sklearn_tags__ is unique to scikit-learn’s ecosystem, so the error is tied to its classes or custom extensions.

What if updating scikit-learn doesn’t fix it?

Check your inheritance chain, super() usage, and ensure all parent classes are initialized properly.

Conclusion: Resolve the Error and Keep Coding

The “‘super’ object has no attribute ‘sklearn_tags'” error might seem daunting, but it’s usually caused by a small oversight in how you’re using super(), inheriting classes, or setting up scikit-learn estimators. By following the steps above—checking your super() calls, verifying parent classes, updating libraries, and debugging systematically—you can fix this Python super error and get back to building awesome machine learning models.

Have you encountered this error in your code? Try one of the solutions above and share your results in the comments. If you’re still stuck, let’s debug it together!

Resource: For more on building custom scikit-learn estimators, visit scikit-learn’s Developer Guide.

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