Random forests reduce the variance seen in decision trees by: Random forestsВ are commonly reported as the most accurate learning algorithm. Enter the random forest-a collection of decision trees with a single, aggregated result. If there was a way to generate a very large number of trees, averaging out their solutions, then youвЂ™ll likely get an answer that is going to be very close to the true answer. A tree generated from 99 data points might differ significantly from a tree generated with just one different data point. Decision trees have high variance, which means that tiny changes in the training data have the potential to cause large changes in the final result.ДЄs noted above, decision trees are fraught with problems. Variance error refers to how much a result will change based on changes to the training set.a linear equation) or by a simple binary algorithm (like the true/false choices in the above tree) will often result in bias. For example, restricting your result with a restricting function (e.g. Bias error happens when you place too many restrictions on target functions.ItвЂ™s possible for overfitting with one large (deep) tree. Overfitting happens for many reasons, including presence of noiseВ and lack of representative instances.However, this simplicity comes with a few serious disadvantages, including overfitting,В error due to biasВ and error due to variance.

They are simple to understand, providing a clear visual to guide the decision making progress.

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