A financial institution wants to predict whether a loan applicant is likely to default on their loan. They have a mix of numerical data (like income, age) and categorical data (like occupation, marital status). Which algorithm might be well-suited for this task due to its ability to handle both types of data?
- Decision Tree
- Random Forest
- Support Vector Machine
- k-Nearest Neighbors
The Random Forest algorithm is well-suited for this task because it can handle both numerical and categorical data effectively. It combines multiple decision trees and takes a vote to make predictions, making it robust and accurate for such mixed data.
Loading...
Related Quiz
- How do residuals, the differences between the observed and predicted values, relate to linear regression?
- The ability of SVMs to handle non-linear decision boundaries is achieved by transforming the input data into a higher-dimensional space using a ______.
- For the k-NN algorithm, what could be a potential drawback of using a very large value of kk?
- You are given a dataset of customer reviews but without any labels indicating sentiment. You want to group similar reviews together. Which type of learning approach will you employ?
- While LSTMs have three gates, the GRU simplifies the model by using only ________ gates.