Pythonic Class Inheritance Demystified
In the realm of Python programming, class inheritance is a fundamental concept in object-oriented programming (OOP) that offers a powerful way to reuse and organise code. This article demonstrates how to utilise class inheritance to create a custom machine learning model for predicting customer churn.
## Defining Parent and Child Classes
To begin, let's define a parent class and a child class. The parent class, often referred to as the base class or superclass, houses the essential properties and methods that the child class will inherit.
```python # Parent Class class MachineLearningClassifier: def __init__(self): pass
def fit(self, X, y): pass
def predict(self, X): pass
# Child Class class CustomClassifier(MachineLearningClassifier): def __init__(self, random_forest_classifier): super().__init__() self.random_forest_classifier = random_forest_classifier
def fit(self, X, y): self.random_forest_classifier.fit(X, y)
def predict(self, X): return self.random_forest_classifier.predict(X) ```
## Benefits of Inheritance
By using inheritance, we can reuse the code from the parent class in our custom child class, reducing redundancy and improving maintainability. Additionally, we can organise our code in a more logical and intuitive manner by creating a hierarchy of classes that represents real-world relationships.
## Using the CustomClassifier
To use the CustomClassifier, we'll define an instance of it and pass a RandomForestClassifier instance as an argument. We can then fit the model to our data, make predictions, and access the feature importance.
```python from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix import seaborn as sns import matplotlib.pyplot as plt
# Load the Telco churn data set # ...
# Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Create an instance of CustomClassifier and fit it to the training data custom_classifier = CustomClassifier(RandomForestClassifier(random_state=42)) custom_classifier.fit(X_train, y_train)
# Make predictions on the testing data y_pred = custom_classifier.predict(X_test)
# Generate a confusion matrix confusion_mtx = confusion_matrix(y_test, y_pred) sns.heatmap(confusion_mtx, annot=True, fmt='d') plt.show()
# Access the feature importance feature_importances = custom_classifier.random_forest_classifier.feature_importances_ print(feature_importances) ```
By extending the functionality of the RandomForestClassifier, we can create a custom machine learning model that is tailored to our specific needs. This approach allows for greater flexibility and customisation when working with existing machine learning packages in Python.
Technology used in Python programming, such as class inheritance in object-oriented programming (OOP), allows for the reuse and organization of code, reducing redundancy and improving maintainability. In this example, we define a parent class, , and a child class, , that extends its functionality to create a custom machine learning model for predicting customer churn.
