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What are Decision Trees?
Decision trees are a popular machine learning algorithm and a visual representation of decision-making processes. They are used for both classification and regression tasks and offer a simple yet powerful way to analyze and interpret data. Decision trees have broad applications across quantitative finance as they can be used in credit risk assessment, trading strategies, portfolio management, fraud detection, credit scoring and loan approval, and option pricing.
How does a decision tree work?
A decision tree is composed of nodes and branches. The nodes represent decision points or features, while the branches represent the possible outcomes or choices based on those features. The tree structure starts from the root node and branches out to subsequent nodes until reaching the leaf nodes, which represent the final predictions or outcomes.
At each node, a decision tree algorithm selects the best splitting criterion based on certain measures, such as Gini impurity or information gain. These criteria evaluate the similarity or purity of the target variable within each potential branch. The goal is to split the data in a way that maximizes the separation between different classes or minimizes the variance in regression tasks.
The algorithm then determines the most informative features for decision-making. It selects the feature that provides the greatest discriminatory power or predictive value. The selection process aims to create branches that result in the most accurate predictions or classifications.
Something to be aware of is that decision trees can be prone to overfitting, where they become overly complex and tailor-made for the training data. To avoid overfitting, pruning techniques are often applied. This involves removing branches that do not significantly contribute to the predictive performance on unseen data.
Decision trees can also be combined using ensemble methods such as Random Forests or Gradient Boosting. These methods create an ensemble of decision trees, each trained on a different subset of the data or with different parameter settings. The ensemble aggregates the predictions of individual trees, resulting in improved accuracy and robustness.
However, decision trees also have limitations. They can be sensitive to small changes in the data, and they may struggle to capture relationships that require multiple levels of decision-making. Nonetheless, decision trees remain a popular and valuable tool in machine learning and data analysis.
Decision trees are covered in more detail in module 4 of the CQF program.