Final answer:
Random forests and boosted trees are ensemble machine learning methods that involve creating multiple decision trees. They are similar in their use of decision trees for predictive tasks but differ in their construction methods: random forests build trees independently with a measure of randomness, while boosted trees build trees sequentially to correct predecessor's errors and can be more prone to overfitting.
Step-by-step explanation:
Random forests and boosted trees are both ensemble learning methods used in machine learning for making predictions. A random forest is a collection of decision trees where each tree is constructed from a random subset of the data and a random subset of the features.
The forest then makes a decision based on the majority vote from all the trees. On the other hand, a boosted tree algorithm combines the output of multiple weak learners (typically decision trees) in a sequential manner, where each tree attempts to correct the errors made by the previous one, with more weight given to the misclassified observations.
The similarities between random forests and boosted trees are that they both involve creating multiple decision trees and are used for classification and regression tasks. However, the key differences lie in how the trees are built and combined. Random forests build each tree independently while boosted trees build trees sequentially.
Another difference is in the way they handle overfitting. Random forests are generally more robust to overfitting due to the randomness introduced during tree construction, whereas boosted trees can overfit if not carefully tuned with parameters such as the learning rate or depth of the trees.