When building a decision tree, the steps generally involve:

Decision tree generation: This is the process where the model iteratively splits the data based on feature values to form branches.

Pruning: This step occurs post-generation, where unnecessary branches are removed to reduce overfitting and enhance generalization.

Feature selection: This is part of decision tree construction, where relevant features are selected at each node to determine how the tree branches.

Data cleansing, on the other hand, is a preprocessing step carried out before any model training begins. It involves handling missing or erroneous data to improve the quality of the dataset but is not part of the decision tree building process itself.

HCIA AI References:

Machine Learning Overview: Includes a discussion on decision tree algorithms and the process of building decision trees.

AI Development Framework: Highlights the steps for building machine learning models, separating data preprocessing (e.g., data cleansing) from model building steps.