Four Factors to consider when choosing b/w Decision Tree and Random Forest

The decision to choose between Random Forest and Decision Tree models depends on the complexity of the problem, the size of the dataset, the interpretability of the model, and the trade-off between accuracy and computational efficiency. Complexity of the problem: Decision trees are simpler and easier to interpret, making them a good choice for smaller and less complex problems. However, for larger and more complex problems, Random Forest models can provide better accuracy due to their ability to combine multiple decision trees. Size of the dataset: Decision trees can be sensitive to noise and outliers, and may overfit the data if the dataset is too small. Random Forest models can be more robust to noise and overfitting, making them a good choice for smaller datasets.