Object-Oriented Programming in Machine Learning

Introduction

When I first tried using Object-Oriented Programming (OOP) in a machine learning project, I struggled so much that I deleted the entire codebase and went back to my usual procedural approach. It felt overwhelming—so many classes, methods, and abstract concepts to manage! But after revisiting it later, I realized that OOP could actually make my code far more structured and reusable, especially for large ML projects.

If you’ve ever faced similar frustrations, this article will help. We’ll go beyond the standard textbook definitions and focus on practical applications, trade-offs, and performance considerations of using OOP in ML. Hopefully, by the end, you’ll feel more confident about when and how to use OOP in your projects.

Why Use OOP in Machine Learning?

ML workflows involve multiple steps—data loading, preprocessing, model training, and evaluation. Without structure, this can quickly turn into a tangled mess of scripts. OOP helps by:

• Encapsulating complexity (e.g., keeping data handling separate from model logic).
• Improving reusability (e.g., reusing the same preprocessing pipeline across different projects).
• Enhancing maintainability (e.g., updating a class without breaking everything).

That said, OOP isn’t always the best choice. Sometimes, a simple functional approach is more efficient. We’ll explore both sides.

Core OOP Principles in ML

1. Encapsulation: Grouping Related Code

Encapsulation helps structure your ML pipeline by grouping related data and methods inside a class. Instead of spreading your preprocessing code across multiple scripts, you can organize it neatly:

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import pandas as pd
from sklearn.model_selection import train_test_split

class Dataset:
    def __init__(self, file_path):
        self.data = pd.read_csv(file_path)
        self.X = None
        self.y = None

    def preprocess(self, target_column):
        self.X = self.data.drop(target_column, axis=1)
        self.y = self.data[target_column]
        return self.X, self.y

    def split(self, test_size=0.2):
        return train_test_split(self.X, self.y, test_size=test_size)

Why This Works:

• Makes preprocessing modular and reusable.
• Allows easy updates without modifying multiple scripts.
• You can swap pd.read_csv with a database query without changing the entire pipeline.

However, for small projects, writing a function instead of a class might be more efficient.

2. Inheritance: Avoiding Code Duplication

Inheritance lets you create a base model class and extend it for specific algorithms:

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from sklearn.base import BaseEstimator
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier

class BaseModel(BaseEstimator):
    def __init__(self, model):
        self.model = model

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

    def predict(self, X):
        return self.model.predict(X)

class RandomForestModel(BaseModel):
    def __init__(self, n_estimators=100):
        super().__init__(RandomForestClassifier(n_estimators=n_estimators))

class NeuralNetModel(BaseModel):
    def __init__(self, hidden_layers=128):
        super().__init__(MLPClassifier(hidden_layer_sizes=(hidden_layers,)))

Performance Considerations:

• OOP overhead: Calling methods and creating objects has a slight performance cost compared to procedural scripts.
• Flexibility gain: In larger projects, this approach saves time and reduces errors when swapping models.
• Memory usage: Large hierarchies can lead to unnecessary memory consumption if not managed well.

3. Polymorphism: Writing Flexible Code

Polymorphism allows different model types to be used interchangeably. This means your train_model function doesn’t need to know if it’s training a neural network or a decision tree:

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def train_model(model: BaseModel, X_train, y_train):
    model.fit(X_train, y_train)
    return model

rf_model = train_model(RandomForestModel(), X_train, y_train)
nn_model = train_model(NeuralNetModel(), X_train, y_train)

Performance Considerations:

• Reduced boilerplate: Avoids duplicate train functions for each model.
• Easier experimentation: Swap models without changing function signatures.

When to Use OOP vs. Functional Programming

Lessons from My Experience

• The first time I tried OOP, I over-engineered everything and made it too complex.
• Later, I found a balance: use OOP for structuring ML workflows but keep performance-critical code simple.
• For small experiments, procedural code is faster and easier.
• In production, OOP shines by making code more maintainable.

Best Practices and Pitfalls

✅ Do This:

✔ Follow SOLID principles—keep classes focused on one responsibility.
✔ Use composition over deep inheritance (e.g., Pipeline(preprocessor, model)).
✔ Extend existing frameworks (e.g., scikit-learn classes) instead of reinventing the wheel.

❌ Avoid This:

❌ Over-engineering small projects—OOP is useful, but don’t force it.
❌ Creating deep inheritance trees—this can cause maintenance nightmares.
❌ Hardcoding dependencies—use abstraction (PreprocessingStrategy instead of StandardScaler).

Final Thoughts

OOP in ML isn’t just about writing fancy classes—it’s about structuring your code so it’s scalable, maintainable, and reusable. However, it’s not always necessary, and in some cases, functional programming is the better choice.

If you’re just starting out with OOP in ML, keep it simple. Don’t try to turn every script into a full-fledged framework. Start small, refactor when needed, and always consider performance trade-offs.

Have you struggled with OOP in ML? Share your experiences!