Difference Between Classification and Prediction

Key Difference – Classification vs Prediction

Classification and predication are two terms associated with data mining. Data is important to almost all the organization to increase profits and to understand the market. Plain data does not have much value. Therefore, the data should be processed in order to get useful information. The data mining is the technology that extracts information from a large amount of data. It helps to get a broad understanding of the data. Some applications of data mining are market analysis, production control and fraud detection. The classification and predication are two terms associated with data mining. This article discusses the difference between classification and predication. Classification is the process of identifying the category or class label of the new observation to which it belongs.  Predication is the process of identifying the missing or unavailable numerical data for a new observation. That is the key difference between classification and predication. The predication does not concern about the class label like in classification.

What is Classification?

Classification is to identify the category or the class label of a new observation. First, a set of data is used as training data. The set of input data and the corresponding outputs are given to the algorithm. So, the training data set includes the input data and their associated class labels. Using the training dataset, the algorithm derives a model or the classifier.  The derived model can be a decision tree, mathematical formula or a neural network. In classification, when an unlabeled data is given to the model, it should find the class which it belongs to.  The new data provided to the model is the test data set.

Classification is the process of classifying a record. One simple example of classification is to check whether it is raining or not. The answer can either be yes or no. So, there is a particular number of choices. Sometimes there can be more than two class to classify. That is called multiclass classification. In real life, the bank needs to analyse whether giving a loan to a particular customer is risky or not. In this example, a model is constructed to find the categorical label. The labels are risky or safe.

What is Predication?

Another process of data analyzing is the predication. It is used to find a numerical output. Same as in classification, the training dataset contains the inputs and corresponding numerical output values. According to the training dataset, the algorithm derives the model or a predictor. When the new data is given, the model should find a numerical output. Unlike in classification, this method does not have the class label. The model predicts a continuous-valued function or ordered value.

Regression is generally used for predication. Predicating the value of a house depending on the facts such as the number of rooms, the total area etc. is an example for predication. A company might find the amount of money spent by the customer during a sale. That is also an example for prediction.

What is the Similarity Between Classification and Predication?

◉ Both Classification and Predication are forms of data analyzing used in data mining.

What is the Difference Between Classification and Predication?

Classification vs Predication
Classification is the process of identifying to which category, a new observation belongs to on the basis of a training data set containing observations whose category membership is known.	Predication is the process of identifying the missing or unavailable numerical data for a new observation.
Accuracy
In classification, the accuracy depends on finding the class label correctly.	In prediction, the accuracy depends on how well a given predictor can guess the value of a predicated attribute for new data.
Model
A model or the classifier is constructed to find the categorical labels.	A model or a predictor will be constructed that predicts a continuous-valued function or ordered value.
Synonyms for the Model
In classification, the model can be known as the classifier.	In prediction, the model can be known as the predictor.

Source: differencebetween.com