Question

Which of the following is a function of exploratory data analysis (EDA)?

1) To evaluate the decisions made by the model after training on the data.
2) To evaluate the organization's project management structure for the AI project.
3) To evaluate the quality of data before it is used to train a model.
4) To evaluate the methods used to collect the data.

Answer 1

The function of exploratory data analysis (EDA) is to evaluate the quality of data before it is used to train a model, which involves turning raw data into information capable of supporting scientific questions or decision-making.

Step-by-step explanation:

The function of exploratory data analysis (EDA) is to evaluate the quality of data before it is used to train a model. This involves analyzing and interpreting the raw data collected during an investigation to determine whether the data can support a hypothesis, which is necessary before applying it in statistical models or AI projects.

EDA ensures that the data sets are suitable for the intended use and can help identify any anomalies or patterns that need to be addressed prior to training a model. The goal is to make sense out of this raw data, turning it into information that can provide evidence in support of a scientific question or decision-making process.

Evaluating decisions made by the model after training, evaluating project management structures, or methods used to collect the data are not direct functions of EDA; these may come at different stages of the data science workflow.