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Decision Trees, Random Forests and Boosting are among the top 16 data science and machine learning tools used by data scientists. The three methods are similar, with a significant amount of overlap. In a nutshell:

- A decision tree is a simple, decision making-diagram.
- Random forests are a large number of trees, combined (using averages or "majority rules") at the end of the process.
- Gradient boosting machines also combine decision trees, but start the combining process at the beginning, instead of at the end.

**Decision Trees and Their Problems**

Decision trees are a series of sequential steps designed to answer a question and provide probabilities, costs, or other consequence of making a particular decision.

They are simple to understand, providing a clear visual to guide the decision making progress. However, this simplicity comes with a few serious disadvantages, including overfitting, error due to bias and error due to variance.

- Overfitting happens for many reasons, including presence of noise and lack of representative instances. It's possible for overfitting with one large (deep) tree.
- Bias error happens when you place too many restrictions on target functions. For example, restricting your result with a restricting function (e.g. a linear equation) or by a simple binary algorithm (like the true/false choices in the above tree) will often result in bias.
- Variance error refers to how much a result will change based on changes to the training set. 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.

**Random Forest vs Decision Trees**

As noted above, decision trees are fraught with problems. A tree generated from 99 data points might differ significantly from a tree generated with just one different data point. If there was a way to generate a very large number of trees, averaging out their solutions, then you'll likely get an answer that is going to be very close to the true answer.

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