Demonstrating the implementation of a decision tree algorithm, which is a type of supervised machine learning algorithm that is used for both classification and regression tasks.
Implementing Decision Trees is the process of building a decision tree model, which is a type of supervised machine learning algorithm used for both classification and regression tasks. It uses a tree-like structure to make predictions based on the values of input features.
The process generally involves the following steps:
- Data preparation: loading and cleaning the data set.
- Training the model: using a training dataset to learn the relationship between the features and the target variable.
- Evaluation: measuring the performance of the model using metrics such as accuracy, precision and recall.
- Visualization: represent the decision tree in a graphical format to help understand the decision-making process of the model.
- Making predictions: using the trained model to make predictions on new data.
The algorithm uses a greedy approach to recursively divide the feature space into smaller subsets and create a decision tree by choosing the feature that results in the most information gain at each step. The tree will continue to split on a feature until a stopping criteria is met. The final result is a tree of decisions, where each internal node represents a test on an attribute, each branch represents the outcome of a test, and each leaf node represents a class label.