Today there was a discussion in Class about decision tree and Random Forest models of data.Thats where I started to think about this and posting some insights on this.
A decision tree is a supervised machine learning algorithm used for both classification and regression tasks. It is a visual representation of a decision-making process, where each internal node of the tree represents a decision or a test on a particular feature, each branch represents an outcome of that decision, and each leaf node represents the final prediction or classification.
Decision tree works like following:
- Splitting Data: The tree starts with a single node that contains all the data. At each internal node, the dataset is split into two or more child nodes based on a feature and a threshold. The goal is to create splits that result in the purest possible child nodes, meaning that they contain similar target values.
- Node Selection: The algorithm selects the feature and threshold that result in the best split, typically using criteria like Gini impurity or information gain for classification tasks and mean squared error for regression tasks.
- Recursion: The splitting process is applied recursively to each child node until a stopping condition is met. This condition could be a predefined depth limit, a minimum number of data points in a node, or the purity of the nodes.
- Leaf Nodes: Once the tree reaches a stopping condition, the leaf nodes make predictions. In classification, the majority class in the leaf node is used as the prediction, while in regression, it’s typically the mean of the target values in the leaf node.
Here are some complexities and challenges that we might encounter:
Complex Decision Trees: Decision trees can become overly complex and overfit the training data. This means they capture noise in the data rather than the underlying patterns.
To reduce overfitting:Prune the tree by limiting the maximum depth or setting a minimum number of samples per leaf node. Pruning involves removing branches that do not significantly contribute to the tree’s predictive power. It can be done by setting a minimum node size, which defines the minimum number of data points required in a node to allow further splitting.
2. Feature Importance:
Bias Towards High Cardinality Features:Decision trees may bias importance toward features with many categories. To address this we consider normalizing feature importance values by the number of categories in the feature.
3. Handling Imbalanced Data:
Biased Predictions: Decision trees can produce biased predictions on imbalanced datasets, favoring the majority class. To handle imbalanced data we adjust class weights or use balanced class sampling.Using other techniques like resampling, specialized algorithms like Random Forest, which often handle imbalanced data better.