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How does a random forest classification model work?
In a Random Forest, where there are several trees, a tree would also be made considering feature 2 as the node. This way, the model picks up nodes in a random manner and makes a forest. These trees are then trained differently on same dataset and they come up with different predictions.
What does random forest mean in machine learning?
Random forest is an ensemble machine learning model. An ensemble machine learning model is a model which is a collection of several smaller models. The Random Forest model of machine learning is nothing but a collection of several decision trees. These trees come together to a combined decision to give the output.
How is a random forest a decision tree?
Random forest is an ensemble of decision trees. This is to say that many trees, constructed in a certain “random” way form a Random Forest. Each tree is created from a different sample of rows and at each node, a different sample of features is selected for splitting.
Why do we call it a random forest?
Random Forest is a tree-based machine learning algorithm that leverages the power of multiple decision trees for making decisions. As the name suggests, it is a “forest” of trees! But why do we call it a “random” forest? That’s because it is a forest of randomly created decision trees.
Is it OK to get 100% accuracy in random forest?
In x_trainset,x_testset i have only the features (7) and in y_trainset,y_testset i have only column “levels” which is needed to be predicted I don’t know whether this is correct.Please can anyone answer this as soon as possible
Are there any R-models giving 100% accuracy?
EDIT: based on the comments, I realized that the no.subjective.fever variable had the exactly same values as the target variable death, so I excluded it from the model. Then I got even stranger results: The 100% accuracy results are probably not correct.