What is a decision tree?Question 8Answera.A neural network used in deep learningb.A linear model used to make predictions in supervised learningc.A clustering algorithm used in unsupervised learningd.A tree-like model used to make predictions in supervised learning

A decision tree is a tree-like model used to make predictions in supervised learning. So, the correct answer is d. A tree-like model used to make predictions in supervised learning.

Here is a step-by-step explanation:

1. A decision tree is a graphical representation of possible solutions to a decision based on certain conditions. It's called a decision tree because it starts with a single box (or root), which then branches off into a number of solutions, just like a tree.

2. The decision tree model is used in supervised learning where the target variable is known. It uses the tree representation to solve the problem in which each leaf node corresponds to a class label and attributes are represented on the path to the root node.

3. Decision trees are used for both classification and regression tasks. In classification, the tree is built with the categorical target variable such as 'yes' or 'no'. In regression, the decision tree is built with a continuous target variable.

4. The main advantage of decision tree is that it's easy to understand and interpret, and it can handle both categorical and numerical data.

5. However, decision trees can easily overfit or underfit the dataset, which can lead to poor prediction performance. To overcome this, techniques such as pruning, setting the minimum number of samples required at a leaf node, or setting the maximum depth of the tree are used.