Random Forests are interesting because they are designed to make many, many bad decisions that cancel each other out leaving only the good decisions behind.
To oversimplify, each of the many, many decision trees is going to do a poor job building a classifier. Each tree only uses a subset of the data and a subset of the features. So, the results of each tree won't generalize well (and may not even do well classifying the data it was trained on). But, for each tree that misclassifies in one direction, another tree will misclassify in the other direction. The theory (which appears to be validated in practice) is that poor decisions cancel each other out but good decisions don't get canceled out.
Although, it isn't quite a simple as a "good" tree and a "bad" tree. All decision trees are bad but there should randomly be good parts in the bad trees. Each tree is going to be "mostly crap" (as Jeremy Howard puts it). But "mostly crap" means "a little good." The crap in one tree cancels out the crap in another tree. The good parts are left behind... and... like magic... you have a good classifier that is fast although somewhat of a black box.
Jeremy Howard from Kaggle is a big believer and where most of my knowledge of Decision Tree Ensemble algorithms come from: https://www.kaggle.com/wiki/RandomForests
To oversimplify, each of the many, many decision trees is going to do a poor job building a classifier. Each tree only uses a subset of the data and a subset of the features. So, the results of each tree won't generalize well (and may not even do well classifying the data it was trained on). But, for each tree that misclassifies in one direction, another tree will misclassify in the other direction. The theory (which appears to be validated in practice) is that poor decisions cancel each other out but good decisions don't get canceled out.
Although, it isn't quite a simple as a "good" tree and a "bad" tree. All decision trees are bad but there should randomly be good parts in the bad trees. Each tree is going to be "mostly crap" (as Jeremy Howard puts it). But "mostly crap" means "a little good." The crap in one tree cancels out the crap in another tree. The good parts are left behind... and... like magic... you have a good classifier that is fast although somewhat of a black box.
Jeremy Howard from Kaggle is a big believer and where most of my knowledge of Decision Tree Ensemble algorithms come from: https://www.kaggle.com/wiki/RandomForests