The difference between regression and classification models lies in the type of output they predict. Lang
Regression
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Goal: Predict a continuous numeric value
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Example Outputs: Price, temperature, age, salary
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Algorithms Used:
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Linear Regression
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Decision Tree Regression
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Random Forest Regressor
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XGBoost Regressor
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Example Question:
What will be the house price given the size and location?
Classification
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Goal: Predict a category or class label
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Example Outputs: Yes/No, Spam/Not Spam, Disease/No Disease
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Algorithms Used:
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Logistic Regression
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Decision Trees
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Random Forest Classifier
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SVM
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XGBoost Classifier
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Example Question:
Will this email be classified as spam or not?
Summary Table
| Feature | Regression | Classification |
| Output Type | Continuous value | Categorical label |
| Example Output | 250.5, 75 kg, 98.7% | Yes/No, Red/Blue, Class A/B |
| Example Algorithm | Linear Regression, SVR | Logistic Regression, SVM |