Predicting which companies are likely to be go bankrupt would be an example of which of the following?

Question

BlackTom AI · Accepted Answer

Question restatement: Predicting which companies are likely to go bankrupt would be an example of which of the following?

Answer options:
- Classification models
- Time series analysis
- Clustering models
- Decision trees

Option 1: Classification models. Here the goal is to assign each company to a category such as 'bankrupt' or 'not bankrupt' based on features (financial ratios, market indicators, etc.). This fits the definition of a classification problem in machine learning, where the output is a discrete label. The reasoning is that bankruptcy status is a categorical outcome, and a classifier learns a decision boundary or probability for each class.

Option 2: Time series analysis. This approach is focused on sequential data and tracking values over time to forecast future values. While time series can be involved in risk assessment, predicting which companies will go bankrupt is typically framed as a label prediction problem at a point in time, not solely as forecasting a time-dependent sequence. Time series would be more central if you were predicting a numerical metric over time rather than categorizing bankruptcy risk at an instance.

Option 3: Clustering models. Clustering is an unsupervised method that groups similar observations without using known labels. Since bankruptcy prediction relies on labeled outcomes (bankrupt vs. non-bankrupt) to learn a mapping from features to a class, clustering does not directly provide a supervised prediction of bankruptcy status. It could be used for exploration or segmentation, but not the primary predictive task described.

Option 4: Decision trees. A decision tree can be used as a classifier (a type of classification model) and is a common method for bankruptcy prediction. However, as a stand-alone description, it specifies a particular model type rather than the general task. It is also true that decision trees can perform classification, but the broader correct categorization for the described task is classification models in general. Hence, this option overlaps with Option 1 but does not capture the broader framing on its own.

In summary, the scenario describes a supervised learning task where the aim is to assign a bankruptcy label to each company based on features, which aligns with classification models. Time series analysis focuses on sequential forecasting, clustering focuses on grouping without labels, and while decision trees can be used for classification, the most fitting umbrella term for the task is classification models.

SU25-BL-BUS-A314-12480 Checkpoint 5

Predicting which companies are likely to be go bankrupt would be an example of which of the following?

查看解析

登录即可查看完整答案

类似问题

MC 8-8 (LO 8-2) Predicting which companies are likely to be fraudulent/not fraudulent or likely to be bankrupt or not bankrupt are examples of... Predicting which companies are likely to be fraudulent/not fraudulent or likely to be bankrupt or not bankrupt are examples of ________ blank .

A machine learning system categorizes agricultural crops based on climatic data, soil conditions, and historical yield patterns to identify which crops are most suitable for cultivation during different seasons. This system is primarily performing which type of AI task?

What is classification?

Which of the following forms of data mining assigns records to one of a predefined set of classes?

Which of the following statements about classification in machine learning are correct?

In the linear classification example, what is the significance of defining curves in the manifold?

What tasks are suitable for the classification techniques?(more than one answer)

We saw various other tasks than forecasting for time series analysis. Which one is examplified below: Determining which instrument is playing from a sound signal.

更多留学生实用工具

考试浏览器助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单