![]() When we using a decision tree model on a given dataset the accuracy going improving because it has more splits so that we can easily overfit the data and validates it. The decision tree has more possibility of overfitting whereas random forest reduces the risk of it because it uses multiple decision trees. Overfitting happens when the models learn fluctuation data in the training data which impacted a negative performance on the new data model, when machine learning model cannot fill well on unseen dataset then that is a sign of overfitting if this error is found on the testing or validation dataset is much for the error on the training dataset. Overfitting: Overfitting is the critical issue in machine learning, when we use algorithms then there is a risk of overfitting which can be considered as a general bottleneck in machine learning. ![]() It is the supervised learning algorithm in machine learning that it uses the bagging method so that there is a combination of learning models which increases the accuracy of results, we can also say that random forests build up many decision trees and that combines together which gives a stable and accurate result, when we are using an algorithm to solve the regression problem in a random forest there is a formula to get an accurate result for each node, whereas the accuracy in the decision tree depends on the number of the correct prediction made divided by total numbers of predictions, as it uses large value attribute at each node, it gives less accurate results decision tree is greedy and it may be deterministic, so if we add one more row or if we take out any row then they give different results. Accuracy: Random forest predicts more accurate results than the decision trees.The advantage of the simple decision tree is that this model is easy to interpret and while building decision trees we aware of which variable and what is the value of the variable is using to split the data, and due to that the output will be predicted fast, on the other hand, the random forest is more complex as there is a combination of decision trees while building a random forest we have to define the number of trees we want to build and how many variables we need at each node. Complexity: The decision tree is a simple series of decisions made to get the specific results, it is used for both classification and regression.The split is done with the highest information that will be taken in the first place, also the process has been continued until all the children nodes have consistent data. Data Processing: The random forest is the combination of multiple decision trees which is the class of dataset, some decision trees out of it may give the correct output and others may not give it correctly, but all trees together predict a correct output, whereas the decision trees use an algorithm to decide node and sub-nodes, a node can be split into two or more sub-nodes, by creating sub-nodes it gives another homogeneous sub-nodes, we can say that the nodes have been increases with respect to the target value.Let us discuss some of the major key differences between Random Forest vs Decision Tree: Key Difference between Random Forest vs Decision Tree
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