What is the Difference Between Classification and Prediction?
🆚 Go to Comparative Table 🆚Classification and prediction are two important techniques in data mining that serve different purposes:
Classification:
- Classification is a technique used to categorize data based on its similarities and identify the class.
- The main goal of classification is to correctly predict the target class for each point in the dataset.
- The accuracy of classification relies on encountering the class label accurately.
- In classification, the model used to classify unknown values is called a classifier.
Prediction:
- Prediction is a technique used to predict missing or unavailable values in a dataset.
- The main goal of prediction is to predict the missing data for a new observation based on the previous data.
- The accuracy of prediction relies on how well the model guesses the value for new data.
- 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:
Feature | Classification | Prediction |
---|---|---|
Purpose | Categorizing data based on similarities or known class labels | Predicting a missing or unknown element (continuous value) of a dataset |
Model Used | Classifier | Predictor |
Output | Category or class label | Continuous value |
Training Set | Categorized data (e.g., records of databases and their class labels) | Data with missing or unknown values |
Accuracy | Measured by how well the class label is predicted | Measured by how well the missing value is estimated |
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.
- Classification vs Regression
- Inference vs Prediction
- Clustering vs Classification
- Hypothesis vs Prediction
- Forecast vs Prediction
- Taxonomy vs Classification
- Nomenclature vs Classification
- Classification vs Tabulation
- Prediction vs Prophecy
- Data Mining vs Machine Learning
- Diagnosis vs Prognosis
- Linear vs Logistic Regression
- Classification vs Binomial Nomenclature
- Predictive vs Prescriptive Analytics
- Observation vs Inference
- Conjecture vs Hypothesis
- Accuracy vs Precision
- Precognition vs Premonition
- Adjective vs Predicate