This learning algorithm is suitable for classification problems, regression problems, feature selection, nonlinear decision boundary and decision-making in dealing with missing values.

1. classification problem: decision tree is very suitable for classification problem, that is, predicting a discrete category label. For example, according to the customer's credit history, age, income and other characteristics to predict whether customers will default on loans.

2. Regression problem: Although decision tree is mainly used for classification, it can also be used for regression problem, that is, to predict a continuous numerical value. For example, the price of a house can be predicted according to its area, location, year of construction and other characteristics.

3. Feature selection: The decision tree will automatically select features during the construction process, which is helpful to identify the features that have the greatest impact on the prediction target.

4. Nonlinear decision boundary: Decision tree can construct complex nonlinear decision boundary, which enables it to capture complex patterns in data.

5. Dealing with missing values: Decision tree algorithm can deal with missing values in data, because it will consider the missing situation of each feature in the construction process.