Both classification and prediction are important techniques in the world of data mining. In this article, we will learn the major difference between Classification and Prediction methods.
What is Classification in Data Mining?
Classification is a data mining function that is used to categorise the data depending on its similarities. The foremost goal of classification is to correctly predict the target class for each point in the data.
What is Prediction in Data Mining?
In the prediction method, we need to predict the missing data for a new observation, depending on the previous data.
Or we can say that the predictive models use comprehended outcomes to create a model that can be used to predict values for new data.
Difference between Classification and Prediction methods in Data Mining
| S.No. | Prediction | Classification |
| 1. | In the prediction technique, we need to predict the missing or unavailable value of the dataset. | The classification technique is used to categorise the data, depending on its similarities and to identify the class. |
| 2. | In the case of prediction, the accuracy relies on how well you guess the value for new data. | In the case of classification, the accuracy relies on encountering the class label accurately. |
| 3. | In prediction, the model which is used to predict the strange value is known as a predictor. | In classification, the model used to classify the unknown value is known as a classifier. |
| 4. | It is created from a training set. | It is also created from a training set. |
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