The correct answer is: C. 2, 3 and 4
Decision trees are a type of machine learning algorithm that can be used for both classification and regression tasks. They work by recursively splitting the data into smaller and smaller subsets until each subset contains only one class or value. The leaves of the tree represent the final predictions, and the branches represent the decisions that were made to reach those predictions.
Decision trees are often used because they are relatively easy to understand and interpret. However, they can also have some drawbacks, such as high bias and variance.
Bias is a measure of how well the model is able to fit the training data. A model with high bias will tend to make the same predictions for all data points, regardless of their actual values. This can lead to poor performance on unseen data.
Variance is a measure of how sensitive the model is to small changes in the training data. A model with high variance will tend to make different predictions for different data points, even if those data points are very similar. This can also lead to poor performance on unseen data.
Decision trees can also suffer from a lack of smoothness of prediction surfaces. This means that the predictions made by the model can be very different for data points that are very close together. This can make it difficult to interpret the model’s predictions and can lead to poor performance on unseen data.
Finally, decision trees can have an unbounded parameter set. This means that there is no limit to the number of features that can be used in the model. This can lead to overfitting and poor performance on unseen data.
In conclusion, decision trees are a powerful machine learning algorithm that can be used for both classification and regression tasks. However, they can also have some drawbacks, such as high bias, variance, and a lack of smoothness of prediction surfaces.