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

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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?

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