REGRESSION :
- The output variable takes continues values
- This predicts a value from a continue set
- Regression involves estimating or predicting a response
- e.g. The price of a house depending on the 'size', 'location' can be some 'numerical value'. This numerical value can be continues.
- Given f : x -> y, if y is real number/continues, then this is a regression problem.
- Regression models makes predictions that answer questions like the following:
- what is the value of a house in Taipei, Taiwan ?
- what is the probability that a user click on this ad ?
- Different types of regression :
- Linear regression
- Logistic regression
- Polynomial regression
- Stepwise regression
- Ridge regression
- Lasso regression
- ElasticNet regression
- and many more ....
CLASSIFICATION :
- The output variable takes class labels
- classification predicts the "belonging" to the class
- classification is identifying group membership
- e.g. The prediction of house price can be classified into 'very costly', 'costly', 'affordable', 'cheap', 'very cheap'. Here each class may correspond to some range of values.
- Given f : x -> y, if y is discrete/categorical variable, then this is classification problem.
- Classification model predict discrete values that ans. the questions like the following:
- Is the given email message spam or not spam ?
- Is this an image of dog, a cat or a hamster ?
- Different classification methods:
- Linear classifier: Logistic regression, Naive Bayes classifier
- Support Vector Machines
- Decision Trees
- Boosted Trees
- Random forest
- Neural Networks
- Nearest neighbor
- and many more ....
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