‘Smote’ Object Has No Attribute ‘fit_sample’ [SOLVED]

‘Smote’ Object Has No Attribute ‘fit_sample’ error happens when fit sample is wrong. Replace fit_sample() use fit_resample() function.

In this article, we’ll look at the whole process with a given example. First, we’ll make the problem happen again, and then we’ll fix it. Aside from that, we will also talk about another important fact about smote.

How To Solve Attributeerror: ‘Smote’ Object Has No Attribute ‘fit_sample’?

AttributeError: ‘SMOTE’ object has no attribute ‘fit_sample’ happens when you are using the wrong function. If you use this code to import SMOTE, you should use fit_resample() instead of fit sample.

Error Replication & Reason

Let’s replicate the same issue with some examples:

from sklearn import datasets
import numpy as np
from imblearn.over_sampling import SMOTE
data_frame = datasets.load_breast_cancer()
X = data_frame.data
y = data_frame.target
print(X.shape,y.shape)
oversample = SMOTE()
X, y = oversample.fit_sample(X, y)
print(X.shape,y.shape)

When we run the code above, we will get the same error (no attribute ‘fit_sample’). Here is a picture of the same thing.

How To Fix?

It will work if we change fit_sample() to fit_resample(). Here is all of the code and what it does.

From:

oversample = SMOTE()
X, y = oversample.fit_sample(X, y)

To:

oversample = SMOTE()
X, y = oversample.fit_resample(X, y)

Complete Source Code

Here’s the complete source code:

from sklearn import datasets
import numpy as np
from imblearn.over_sampling import SMOTE
data_frame = datasets.load_breast_cancer()
X = data_frame.data
y = data_frame.target
print(X.shape,y.shape)
oversample = SMOTE()
X, y = oversample.fit_resample(X, y)
print(X.shape,y.shape)

Output

Conclusion

Python has powerful high-level data structures and an easy-to-use but effective way to program with objects. The way Python’s commands are written is a big plus. Its clarity, ease of understanding, and ability to be typed in a variety of ways make it an ideal language for scripting and app development in a wide range of fields and on all platforms.

The approach described above is a simple way to fix the major error “AttributeError: ‘SMOTE’ object has no attribute ‘fit_sample’,” which was mentioned above.

Recommendations

By the way if you encounter an error about importing libraries, I have here the list of articles made to solve your problem on how to fix error in libraries.

Inquiries

However, If you have any questions or suggestions about this tutorial ‘Smote’ Object Has No Attribute ‘fit_sample’, Please feel to comment below, Thank You and God Bless!

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Common use cases for ‘Smote’ Object Has No Attribute ‘fit_sample’ [SOLVED]

• Data pipelines. Python is the standard for ETL, data analysis, and ML workflows.
• Web development. Django and FastAPI power modern web backends and APIs.
• Automation and scripting. System administration, file processing, web scraping, and cron jobs.
• Machine learning. scikit-learn, PyTorch, TensorFlow, Hugging Face for AI/ML projects.
• Educational tools. Python’s readability makes it the go-to teaching language.

Working code example

from typing import Optional

def process_data(items: list[dict]) -> Optional[dict]:
    """Process a list of items and return summary stats."""
    if not items:
        return None
    return {
        "count": len(items),
        "total": sum(item.get("value", 0) for item in items),
        "avg": sum(item.get("value", 0) for item in items) / len(items),
    }

# Usage
data = [{"value": 10}, {"value": 20}, {"value": 30}]
summary = process_data(data)
print(summary)  # {'count': 3, 'total': 60, 'avg': 20.0}

Best practices

• Use type hints. list[dict], Optional[str], and TypedDict make code self-documenting and enable static analysis.
• Follow PEP 8. Consistent style improves readability. Use black or ruff to auto-format.
• Prefer f-strings. f”{value}” is cleaner than str.format() or % formatting.
• Write tests with pytest. Aim for 70%+ coverage on business-critical modules.
• Use ruff or pylint. Static analysis catches many bugs before code runs.

Common pitfalls

• Mutable default arguments. def f(x=[]) reuses the same list across calls. Use x=None then check.
• Integer division. 5/2 gives 2.5 in Python 3. Use // for floor division.
• Missing self on methods. Class methods need self as first parameter.
• Late binding closures. Loops that create lambdas can capture variables late.

Debugging Python code effectively

• print() with context. Add variable names and types: print(f”user_id={user_id} type={type(user_id)}”)
• pdb / breakpoint(). Call breakpoint() anywhere to drop into interactive debugger.
• VS Code debugger. Set breakpoints in the editor, run F5, step through with F10.
• logging over print. import logging; logging.debug() is toggleable and thread-safe for production.
• Read full tracebacks. The bottom-most line usually shows what happened; the stack shows how you got there.

Modern Python tooling

• uv. Ultra-fast package installer and resolver (10-100x faster than pip). Standard in 2026.
• ruff. Fast linter + formatter (replaces flake8, black, isort in one binary).
• mypy. Type checker. Add types incrementally to catch bugs at design time.
• pytest. Standard test framework. Simpler than unittest.
• rich. Beautiful terminal output for CLI tools.

Where to go next after this tutorial

• Learn a web framework. Django for full-stack apps; FastAPI for APIs; Streamlit for data dashboards.
• Study a data library. pandas for data analysis; polars for large-scale processing; DuckDB for embedded SQL analytics.
• Practice with real projects. Browse itsourcecode.com Python Projects for 250+ capstone-ready systems (LLM apps, ML models, chatbots, dashboards).
• Read PEP 20 (Zen of Python). import this in an interpreter to see 19 lines of Python philosophy.

Angel Jude Suarez

Full-Stack Developer at PIES IT Solution

Focuses on Python development, machine learning, and AI integration. Has built production AI systems including OpenAI Whisper integration for medical transcription and GPT-4o-powered diagnosis assistance. Strong background in pandas, scikit-learn, and TensorFlow.

Expertise: Python · PHP · Java · VB.NET · ASP.NET · Machine Learning · AI Integration · OpenCV · Django · CodeIgniter
 · View all posts by Angel Jude Suarez →