Decision Trees and Random Forests

Decision Trees and Random Forests

Description/Paper Instructions

Decision Trees and Random Forests

Decision Trees:

Decision trees are a popular machine learning algorithm used for both classification and regression tasks. They are a type of supervised learning method that learns a hierarchical structure of if-then-else rules from the training data.

The decision tree starts with a single node called the root node, which represents the entire dataset. The root node is split into child nodes based on the values of a selected feature. Each child node represents a subset of the data that satisfies a specific condition. This process of splitting continues recursively until a stopping criterion is met, such as reaching a maximum depth or a minimum number of samples in a node.

The splits in a decision tree are determined based on a measure of impurity or information gain. Impurity measures, such as Gini impurity or entropy, quantify the degree of impurity or disorder in a set of samples. The goal is to find splits that maximize the reduction in impurity, leading to more homogeneous subsets of data at each node.

Once the decision tree is built, the prediction process involves traversing the tree from the root node to a leaf node based on the feature values of the input sample. Each internal node represents a decision based on a feature, and each leaf node represents a predicted class or value.

Decision trees have several advantages. They are easy to interpret and visualize, as the resulting rules can be easily understood by humans. They can handle both categorical and numerical features, and they are robust to missing values. However, decision trees are prone to overfitting, especially when the tree becomes too deep and complex.

Random Forests:

Random forests are an ensemble learning method that combines multiple decision trees to make predictions. The idea behind random forests is to create an ensemble of decision trees and aggregate their predictions to obtain a more accurate and robust model.

The random forest algorithm introduces two sources of randomness: random sampling of the training data and random feature selection. During the training phase, each tree in the random forest is trained on a bootstrap sample of the original dataset. A bootstrap sample is created by randomly sampling the data with replacement, which means some samples may appear multiple times, while others may be left out.

In addition to random sampling, random forests also randomly select a subset of features at each node when finding the best split. This process reduces the correlation between the trees and prevents a few dominant features from overshadowing the others.

Once all the trees are trained, predictions are made by aggregating the predictions of individual trees. For classification tasks, the most common aggregation method is majority voting, where each tree’s prediction is counted as a vote, and the class with the most votes is selected as the final prediction. For regression tasks, the predictions of all trees are averaged to obtain the final prediction.

Random forests offer several advantages over single decision trees. They are less prone to overfitting because the ensemble of trees reduces the variance and provides more robust predictions. They can handle high-dimensional datasets with a large number of features. Random forests can also provide measures of feature importance, which can help in understanding the importance of different features in the prediction process.

In summary, decision trees are individual models that learn hierarchical if-then-else rules from the data, while random forests combine multiple decision trees through random sampling and random feature selection to create a more accurate and robust ensemble model. Random forests address some of the limitations of decision trees and have become a popular choice in machine learning due to their strong predictive performance and interpretability.

Decision Trees and Random Forests

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