Difference Between Classification and Prediction in Data Mining

Data Mining | Classification Vs. Prediction: In this tutorial, we will learn about the concepts of classification and prediction in data mining, and difference between classification and prediction. By Palkesh Jain Last updated : April 17, 2023

What is Classification?

The world of data mining is known as an interdisciplinary one. It requires a range of disciplines such as analytics, database systems, machine learning, simulation, and information sciences. The classification of the data mining system allows users to understand the system and to align their criteria with such systems. Classification is about the discovery of a model that distinguishes groups and concepts of data. The definition is to forecast the class of objects by using this model. The derived model relies on the study of training data sets.

A classification task starts with a data set where the assignments of the class are known. For example, based on observable data for multiple loan borrowers over some time, a classification model may be established that forecasts credit risk. The data could track job records, homeownership or leasing, years of residency, number, and type of deposits, in addition to the historical credit ranking, and so on. The goal would be credit ranking, the predictors would be the other characteristics, and the data would represent a case for each consumer.

How Does Classification Works?

The functioning of classification with the assistance of the bank loan application has mentioned above. There are two stages in the data classification system are classifier or model creation and using classification classifier.

  • Classifier or model creation:
    This level is the learning stage or the learning process. The classification algorithms construct the classifier in this stage. A classifier is constructed from a training set composed of the records of databases and their corresponding class names. Each category that makes up the training set is referred to as a category or class. We may also refer to these records as samples, objects, or data points.
  • Using classifier for classification:
    The classifier is used for classification at this level. The test data are used here to estimate the accuracy of the classification algorithm. If the consistency is deemed sufficient, the classification rules can be expanded to cover new data records.
  • Data Classification Process:
    The data classification process can be categorized into five steps:
    1. Create the goals of data classification, strategy, workflows, and architecture of data classification.
    2. Classify confidential details that we store.
    3. Using marks by data labelling.
    4. To improve protection and docility, use effects.
    5. Data is complex, and a continuous method is a classification.

What is a Prediction?

To detect the inaccessible data, it uses regression analysis and detects the missing numeric values in the data. If the classmark is absent, so classification is used to render the prediction. Due to its relevance in business intelligence, the prediction is common. If the classmark is absent, so the prediction is performed using classification.

There are two methods of predicting data. Due to its relevance in business intelligence, the prediction is common. Examples of situations where the role of data processing is prediction are below.

Suppose the marketing manager needs to predict how much a particular customer will spend at his company during a sale. We are bothered to forecast a numerical value in this case. Therefore, an example of numeric prediction is the data processing activity. In this case, a model or a predictor will be developed that forecasts a continuous or ordered value function.

Comparison of classification and prediction methods

Comparison of classification and prediction methods are described below -

  1. Accuracy -
    Classifier accuracy refers to the classifier's ability. It correctly predicts the class label and the predictor's accuracy refers to how well a given predictor can estimate the value of a new data attribute predicted.
  2. Speed -
    This refers to the expense of producing and using the classifier or predictor for estimation.
  3. Robustness -
    It refers to the classifier or predictor's ability to make correct predictions from the noisy data given.
  4. Scalability -
    It refers to the capacity to effectively build the classifier or predictor, given a large amount of data.
  5. Interpretability -
    It refers to the extent to which the classifier or predictor knows.

Difference between classification and prediction

The decision tree, applied to existing data, is a classification model. We can get a class prediction if we apply it to new data for which the class is unknown. The assumption is that the new data comes from a distribution similar to the data we used to construct our decision tree. This is a correct assumption in many instances, which is why we can use the decision tree to build a predictive model. Classification of prediction is the process of finding a model that describes the classes or concepts of information. The purpose is to be able to predict the class of objects whose class label is unknown using this model.

Classification and Prediction Issues

Followings are the key challenge in classification and data prediction -

  • Data Cleaning -
    The cleaning of data entails the elimination of noise and recovery of lost values. Through applying key techniques, the noise is eliminated and the issue of missing values is solved by substituting the missing value for that attribute for the most frequently occurring value.
  • Relevance Analysis -
    The database can also have meaningless properties. Correlation analysis is used to assess if two features are correlated with each other.
  • Normalization & Generalization -
    By generalizing, the data may also be translated. When the neural networks or the techniques requiring tests are used in the learning process, normalization is used. The data is converted using normalization. To make them fall into a limited defined range, normalization requires scaling all values for given attributes.

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