How do you interpret and visualize the results of a classification or regression tree?

1 The structure of a CART

A CART is a binary tree that consists of nodes, branches, and leaves. A node is a point where the data is split into two subsets based on a feature and a threshold. A branch is a connection between two nodes, and a leaf is a terminal node that represents an outcome or a prediction. The root node is the first node that splits the entire data set, and the internal nodes are the ones that split the subsets further. The depth of a tree is the number of splits from the root node to the leaves.

2 How to read a CART

To read a CART, you start from the root node and follow the branches according to the rules or criteria. For example, if the root node splits the data based on the feature X and the threshold 10, you follow the left branch if X <= 10, and the right branch if X > 10. You repeat this process until you reach a leaf node, which gives you the outcome or the prediction for that subset of data. For a classification tree, the outcome is a class label, and for a regression tree, the outcome is a numerical value.

3 How to visualize a CART

To visualize a CART, you can use various tools and libraries, such as scikit-learn, rpart, or plotly in Python or R. These tools can help you generate a graphical representation of a CART, where each node is labeled with the feature and the threshold, and each leaf is labeled with the outcome or the prediction. You can also customize the appearance of the tree, such as the color, shape, size, and font of the nodes and leaves. Some tools can also show you the distribution of the data or the performance metrics of the tree, such as the accuracy, the error, or the impurity.

4 How to interpret a CART

To interpret a CART, you can use various methods and techniques, such as pruning, feature importance, or decision paths. Pruning is a way to reduce the complexity and the overfitting of a tree by removing some nodes or branches that do not contribute much to the accuracy or the error. Feature importance is a way to measure how much each feature contributes to the splitting of the data or the prediction of the outcome. Decision paths are a way to trace the rules or criteria that lead to a specific outcome or prediction for a given observation or instance.

5 How to improve a CART

To improve a CART, you can use various parameters and options, such as the criterion, the max_depth, the min_samples_split, or the min_samples_leaf. The criterion is the function that determines how to split the data at each node, such as the Gini index or the entropy for classification, or the mean squared error or the mean absolute error for regression. The max_depth is the maximum number of splits from the root node to the leaves, which controls the complexity and the overfitting of the tree. The min_samples_split is the minimum number of samples required to split a node, which prevents too many or too small splits. The min_samples_leaf is the minimum number of samples required to be at a leaf node, which prevents too many or too sparse leaves.

6 How to compare and contrast CART

To compare and contrast CART with other machine learning methods, you can use various aspects and criteria, such as the advantages, the disadvantages, the assumptions, or the applications. Some of the advantages of CART are that they are easy to understand and interpret, they can handle both categorical and numerical features, they can deal with missing values and outliers, and they can capture non-linear relationships and interactions. Some of the disadvantages of CART are that they are prone to overfitting and instability, they can be biased towards features with more levels or values, they can have high variance and low bias, and they can be sensitive to noise and small changes in the data. Some of the assumptions of CART are that the data is independent and identically distributed, that the features are relevant and informative, and that the splits are binary and orthogonal. Some of the applications of CART are in domains such as medicine, finance, marketing, or education.