Decision trees are a popular and intuitive machine learning algorithm used for both classification and regression algorithms. They are a tree-like model where an internal node represents a decision based on the value of a particular feature, and each leaf node represents the prediction or the outcome.
Advantages of using decision trees :
-They are simple to understand and interpret as the tress can be visualized.
-Able to handle multi-output problems.
-Possible to validate a model using statistical tests. That makes it possible to account for the reliability of the model.
How decision trees are used :
- Classification:
- Training: Given a dataset with labeles , the decision tree algorithm recursively splits the data based on features to create a tree.
- Prediction: For a new data point, it traverses the tree, making decisions at each node based on feature values until it reaches a leaf node, providing the predicted class.
- Regression:
- Training: Similar to classification but applied to tasks where the output is a continuous value.
- Prediction: The tree predicts a continuous value by averaging the target values of the instances in the leaf node.
- Handling Missing Values:
- Decision trees can handle missing values in the data by selecting the best available split based on the available features.