Explanation of regression and classification problems with examples in data analysis.
1. We collect a set of data on the financial performances and other characteristics of Fortune 500 companies.
Regression; Inference: This is a regression problem since we are trying to understand the relationship between variables (characteristics) and stock prices, which involve predicting a continuous quantity.
2. We would like to develop a spam filter which predicts whether an incoming email is junk mail based on the usage of words.
Classification; Prediction: This is a classification problem as we are categorizing emails as spam or non-spam, which is a discrete outcome, and our goal is prediction.
3. We are about to launch a new product and want to know whether it will be a success or failure based on past similar products.
Classification; Prediction: This is a classification problem as we are classifying the new product as a success or failure, and our primary interest is prediction.
4. We collect data on booking records for flights and want to identify factors determining customer show-up for flights.
Regression; Inference: This is a regression problem as we seek to understand the factors influencing a continuous outcome (customer show-up), with a focus on inference.
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