decision tree
concept
Decision Tree is a common machine learning algorithm used for classification and regression tasks. It is a tree-like structure in which each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents an output label or value.
The process of building a decision tree
The process of building a decision tree typically involves the following steps:
- Data preparation and preprocessing:
- Data collection: Obtain and organize the data sets needed for training, including features and target variables.
- Data cleaning: Deal with data problems such as missing values, outliers and duplicate values.
- Feature Engineering: Extract, select, or transform features so that they are suitable for use in decision tree models.
- Feature selection:
- Select dividing features: Select the best features to divide the data set according to a certain metric (such as information gain, Gini coefficient, etc.), so that each division can be as efficient as possible Increase data purity.
- Data segmentation based on selected features: Divide the data set based on selected features to generate subsets.
- Build a decision tree:
- Recursively build subtrees: Recursively apply the steps of feature selection and data segmentation to each subset to build the entire decision tree.
- Determine stopping conditions: For example, the depth of the tree reaches the preset maximum depth, the number of samples contained by the node is less than the threshold, there are no more features available for segmentation, etc.