Random forests

Definition

Random forests is a machine learning algorithm used for both classification and regression tasks that operates by constructing multiple decision trees during training time and outputting the mode of their predictions for classification or the mean prediction for regression. This technique helps improve predictive accuracy and control over-fitting by averaging the results of several trees, which makes it a powerful supervised learning method.

5 Must Know Facts For Your Next Test

1. Random forests reduce overfitting by averaging the results from multiple decision trees, making the final model more robust compared to individual trees.
2. The algorithm uses a technique called 'bootstrap aggregating' or 'bagging,' where it samples subsets of data with replacement to create diverse decision trees.
3. Each tree in a random forest is constructed using a random subset of features, which helps ensure that the model captures various aspects of the data and reduces correlation between trees.
4. Random forests can handle both numerical and categorical data, making them versatile for different types of predictive modeling tasks.
5. Feature importance can be derived from random forests, allowing users to identify which features are most influential in making predictions.

Review Questions

• How does the structure of random forests enhance its predictive performance compared to a single decision tree?
  • The structure of random forests enhances predictive performance by combining multiple decision trees, which individually may suffer from high variance. By averaging their predictions, random forests mitigate the impact of any single tree's errors, resulting in improved accuracy and stability. Additionally, since each tree is built on a different subset of data and features, the overall model captures more diverse patterns in the data.
• Discuss how the concept of bagging contributes to reducing overfitting in random forests.
  • Bagging, or bootstrap aggregating, contributes to reducing overfitting in random forests by creating multiple models trained on different subsets of the original dataset. Each model is trained independently, which means they may capture different aspects of the data. When these models are combined through averaging or voting, it smooths out individual model predictions and minimizes the risk of capturing noise from the training set, leading to better generalization on unseen data.
• Evaluate the role of feature selection in random forests and how it impacts model interpretability.
  • Feature selection plays a significant role in random forests as it influences both model performance and interpretability. Since random forests randomly select features for each tree, they inherently perform feature selection as part of their training process. This can highlight which features are most important for making predictions. Understanding feature importance helps users interpret the model by identifying key drivers behind predictions, thus providing valuable insights into the data without needing complex post-hoc analysis.

Related terms

Decision Tree: A flowchart-like structure used in machine learning that makes decisions based on answering a series of questions, leading to predictions at the leaves.

Bagging: A technique used to improve the stability and accuracy of machine learning algorithms by training multiple models on different subsets of the data and combining their outputs.

Overfitting:

A modeling error that occurs when a machine learning model learns the training data too well, capturing noise along with the underlying pattern, leading to poor performance on unseen data.