What Is Classification Analysis?

Written by Indicative Team


Classification Analysis Defined

Classification analysis is a data analysis task within data-mining, that identifies and assigns categories to a collection of data to allow for more accurate analysis. The classification method makes use of mathematical techniques such as decision trees, linear programming, neural network and statistics.

Classification analysis can be used to question, make a decision, or predict behavior through the use of an algorithm. It works by developing a set of training data which contains a certain set of attributes as well as the likely outcome. The job of the classification algorithm is to discover how that set of attributes reaches its conclusion.

There are two steps in the construction of a classification model.

  • Learning Step – this is where different algorithms are used to build a classifier by making the model learn using the training set available. The model has to be trained for the prediction of accurate results.
  • Classification Step: this is where the model used to predict class labels, tests the constructed model on test data. Which in turn estimates the accuracy of the classification rules.

Some scenarios where classification is used includes; predicting the weather and analyzing health conditions.

In Data Defined, we help make the complex world of data more accessible by explaining some of the most complex aspects of the field.

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