Pros and Cons Decision Tree

Pros

• Do not need to scale and normalize data
• Handles missing values very well
• Less effort in regards to preprocessing

Cons

• Very prone to overfitting
• Sensitive to outliers and changes in the data
• Takes a long time to train and expensive complexity wise
• Weak in terms of regression

Pros and Cons Random Forest

Pros

• Performs well on imbalanced data
• Works well with high dimensionality and handling a large amount of data
• Decorrelates trees, can deal with problem of variance
• Solves classification and regression issues

Cons