Advantages:
Decision Trees are easy to explain. It results in a set of rules. （容易解釋）
It follows the same approach as humans generally follow while making decisions.
Interpretation of a complex Decision Tree model can be simplified by its visualizations. Even a naive person can understand logic.（復雜的決策樹也容易可視化）
The Number of hyper-parameters to be tuned is almost null.
Disadvantages:
There is a high probability of overfitting in Decision Tree. （很容易過擬合）
Generally, it gives low prediction accuracy for a dataset as compared to other machine learning algorithms.（通常情況下精確度不如其他算法好）
Information gain in a?decision tree with categorical variables gives a biased response for attributes with greater no. of categories.
Calculations can become complex when there are many class labels. （分類較多的是否計算比較復雜）
