Data classification is an important route to take while analyzing data because it helps the data scientists to slice and dice data as needed for analysis. Through the use of a classification model use, we are able to convert data from it’s input from (raw data) to analysis/ analyzed data (Tan et al., 2013). Classifying data through use of their attributes and the data’s ability to labeled and presented in various ways, makes the classifying of data an important data processing tool to classify and present data as needed by the stakeholders (Tan et al., 2013).
Another basic concept includes the prescriptive and descriptive models and types of data (Tan et al., 2013). Prescriptive analysis and data allows for the data scientists to be able to use data analysis and prescribe what could happen in the future and shape decisions, however, descriptive analysis includes using the data to ensure that there are descriptive ways of explaining how the business got there, it is more historic looking (Tan et al., 2013).
Through the use of a training set, the learning algorithm is used to feed data into the model (sometimes also called the learned model). The model has the ability to sort, classify, and label data to accurately analyze data based on models requiring data classification (Tan et al., 2013). Through the induction and deduction model being applied to the data, allows for analysis and classification based on attribute and label application (Tan et al., 2013).
A decision tree is a diagram used by data scientists to be able to visualize the data and ensure that the data is classified appropriately for analysis (Tan et al., 2013). A tree modifier includes root nodes, model nodes, and classifiers to be able to make a decision on classifying data. The ability to use decision trees helps with applicability of the decision tree and the data to the business challenges that it could solve, makes the decision tree accessible to all and easy to read, making it expressional in nature (Tan et al., 2013), it helps with handling the missing attributes (as the decision tree is being built for the data scientists to review), and allows for opportunities to identify missing values when the decision tree is being reviewed for data management (Tan et al., 2013).
A hyper parameter is a type of machine learning parameter that enables the implementation of the learning process (Tan et al., 2013). Any value that is assigned to the learning parameter then, it is able to influence the learning process.
Sometimes, there could be a mix in the data that was used for testing and the data that was used as part of a training set. This overlap could cause issues while running the data set through the classification/ attribute model, making it a challenge (Tan et al., 2013). Another challenge is the type of the error that is classified. Validation error could come from sample estimates that may not have made it to the training data set however, the generalization error could include the errors from estimating independent test data to avoid over sitting (Tan et al., 2013).
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