Decision Tree vs Random Forest: Introduction

Decision trees, with their elegant simplicity and transparency, stand in stark contrast to the robust predictive power of Random Forest, an ensemble of trees. In this post, we compare the key distinctions, advantages, and trade-offs between these two approaches. We will use Scikit-Learn for training and testing both models and also perform hyperparameter optimisation to find both model parameters for improved performance.

Machine Learning with Scikit-learn

Scikit-learn (often called sklearn) is a versatile and comprehensive machine-learning library in Python. It offers a rich collection of tools and functions for building, training, and evaluating machine learning models.

Scikit-learn has a variety of supported algorithms. It covers various machine-learning tasks, including classification, regression, clustering, dimensionality reduction, model selection, and more. Scikit-learn provides a solid foundation for machine learning experiments, from data preprocessing to model evaluation.

Scikit-learn also provides helpful tools for data splitting, cross-validation, hyperparameter tuning and metrics for assessing model performance.

You can install scikit-learn and its dependencies using pip, a popular Python package manager. Open your terminal or command prompt and enter the following command to install scikit-learn:

pip install scikit-learn

Once installed, you can import scikit-learn into your Python code using the following import statement:

import sklearn

We have a well-defined interface to scikit-learn machine learning functionality with the following methods that can be used with different algorithms:

# Fit the model to the training data
model.fit(X_train, y_train)

# Make predictions on the test data
y_pred = model.predict(X_test)

Hyperparameter optimisation techniques

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Random Forests versus Decision Trees

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Conclusion: Decision Tree vs Random Forest

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References

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