Out-of-bag error
Hey kiddo! Have you ever heard about out-of-bag error? Well, let me explain it to you.
When we use a technique called Random Forest to train a model, we generate many Decision Trees. By the way, a Decision Tree is a model that uses a set of rules to classify or predict something.
Each of these trees makes predictions for each sample in our dataset. Some samples are used to train the tree, and others are left out of the training process. We call these samples "out-of-bag" samples because they are not used to train that particular tree.
Now, we can use these out-of-bag samples to check how accurate our model is. This is called out-of-bag error.
In simpler words, we create many trees to make predictions, and these trees use a set of data to learn. Once they learned that data, they try making predictions with another data set. But we still have some data that they haven't seen yet, which is the out-of-bag samples. By checking how well these out-of-bag samples perform, we can figure out how accurate our model is.
When we use a technique called Random Forest to train a model, we generate many Decision Trees. By the way, a Decision Tree is a model that uses a set of rules to classify or predict something.
Each of these trees makes predictions for each sample in our dataset. Some samples are used to train the tree, and others are left out of the training process. We call these samples "out-of-bag" samples because they are not used to train that particular tree.
Now, we can use these out-of-bag samples to check how accurate our model is. This is called out-of-bag error.
In simpler words, we create many trees to make predictions, and these trees use a set of data to learn. Once they learned that data, they try making predictions with another data set. But we still have some data that they haven't seen yet, which is the out-of-bag samples. By checking how well these out-of-bag samples perform, we can figure out how accurate our model is.
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