Note the basic concepts in data classification.

Data classification is the process of organizing data by categorizing it. Classification makes it easy to locate and retrieve data (Umadevi & Marseline, 2017). It eliminates data duplication and enhances data security. It also speeds up the data search process and reduces storage costs.

Discuss the general framework for classification.

A lot of accuracy is needed in data used to plan operations and supply chain activities. Accurate data forms the basis for evaluating planning decisions. The general framework provides quality information for decision-making purposes.

What is a decision tree and decision tree modifier? Note the importance.

A decision tree represents possible decisions based on certain conditions. Decision trees assign a specific value to each problem, outcome, and decision path (Patel & Prajapati, 2018). A decision tree helps in crucial business decisions like new product development and new marketing strategies.

What is a hyper-parameter?

Hyper-parameter is a prior parameter that needs to be tuned to optimize. It captures a prior belief before the actual data is observed. It can be referred to as a good guess which is observed without using the actual data.

Note the pitfalls of model selection and evaluation.

When learning data, one can predict the future by interpreting the nature of data or use predictive performance by choosing a model as machinery. The selected model may beat competitors, while predictive performance can be the most appropriate option available.