What is the Difference Between Classification and Prediction?

Classification and prediction are two important techniques in data mining that serve different purposes:

Classification:

1. Classification is a technique used to categorize data based on its similarities and identify the class.
2. The main goal of classification is to correctly predict the target class for each point in the dataset.
3. The accuracy of classification relies on encountering the class label accurately.
4. In classification, the model used to classify unknown values is called a classifier.

Prediction:

1. Prediction is a technique used to predict missing or unavailable values in a dataset.
2. The main goal of prediction is to predict the missing data for a new observation based on the previous data.
3. The accuracy of prediction relies on how well the model guesses the value for new data.
4. In prediction, the model used to predict unknown values is called a predictor.

In summary, classification is about assigning data points to specific categories based on their characteristics, while prediction is about estimating unknown values for new observations. Both techniques are created from a training set, with classification using a classifier and prediction using a predictor.

Comparative Table: Classification vs Prediction

Here is a table outlining the key differences between classification and prediction:

In summary, classification is about categorizing data based on its similarities or known class labels, while prediction is about predicting a missing or unknown element (continuous value) of a dataset. Classification uses a classifier model, which is built from a training set composed of records of databases and their class labels. On the other hand, prediction uses a predictor model, which is constructed from a training set with missing or unknown values. The accuracy of a classifier is measured by how well it predicts the class label, while the accuracy of a predictor is measured by how well it estimates the missing value.