Decision trees are widely used machine learning techniques for predictive modeling and analysis.
A concept proposed by Leo Breiman to refer to decision tree algorithms, which can be used for predictive modeling problems of classification or regression, is classification and regression trees or carts as short.
A classification and regression tree or cart are often employed in machine learning courses as a predictive algorithm. It illustrates how to predict the values of a focus variable based on other attributes. This algorithm is traditionally referred to as the “decision trees,” but it is referred to by the more technical name cart on specific platforms such as R.
Any root node of the tree represents a single input variable (x) used with a split point on that variable (assuming that a variable is a number). The tree’s leaf nodes have an output variable (y) that is used to give predictions. Provide a dataset with two input variables (x) as weight (in KG) and height (in CM), the predicted output of gender is a man or woman.
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The cart algorithm is the basis for widely used algorithms such as the bagged decision trees, boosted decision trees, and random forests. In classification and regression trees, any root node of the tree represents a single input variable (x) used with a split point on that variable (assuming that a variable is a number). The tree’s leaf nodes have an output variable (y) that is used to give predictions. Become a Data Scientist with 360DigiTMG Data Scientist Course in Chennai. Get trained by the alumni from IIT, IIM, and ISB.
Learn a cart model from data
Developing a cart-model includes the selection of input variables with split points upon these variables until a proper tree is developed. A greedy algorithm is used to choose the input variables and breakpoints to minimize cost functions. Tree development ends with a predetermined stop criterion, like a minimum number of training instances allocated to each tree leaf node.
Advantages:
In contrast to other algorithms, decision trees take less amount of effort for pre-processing data.
A decision tree needs no data normalization.
A decision tree often does not require data scaling.
Missing data values often do NOT have any significant effect on the procedure of making a decision tree.
A decision tree model is quite intuitive and straightforward for technical staff and other stakeholders to understand.
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Disadvantages:
A minor variation in the data will lead to a significant change in the decision tree’s structure, creating uncertainty.
Compared to other algorithms, the calculation can sometimes also get even more complicated with a decision tree.
The decision tree also takes more time for the model to be trained.
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Limitations of Classification and Regression Trees – Machine Learning
1.Overfitting: Overfitting happens when a lot of noise that resides in the data and ends up with an incorrect outcome is considered by the tree.
2. High variance: A slight variation in the data, in this case, will lead to a very large variance in the estimation, thereby impacting the reliability of the result.
3. Low bias: Typically, a very complicated decision tree has a low bias, making it hard for the model to integrate some new details.
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