Thus machine learning becomes truly powerful when it is “statistical learning”, where our prediction of “yes” or “no” is associated with a probability (a number ranging from 0 to 1, which can be scaled to generate a score).
Here are some applications we can apply binary classification to:
1. Click prediction, which can be applied to (1) search ranking (2) online ads ranking
If you can rank the probability of user click, then you can present the search results based on that probability. The documents or products what have high probably of being clicked will ranked higher. Similarly, when we decide which display ads toshow to the user, we can use click probability.
2. Fraud detection
Our task is deciding whether a transaction is fraud. This is a simple binary classification: fraud or not? When the probability crosses a threshold (say 0.8), we can classify a transaction as a fraud.
3. Select sales prospects to call based on their probability of responding.
This is also a simple binary classification problem: Predict whether a prospect will respond to not.
Additional applications of binary classification are:
4. Accept or reject an application for loan or credit card, or an insurance claim
5. Customer churn
6. Most valuable customer
7. Cancer detection
8. Quality control: Car is good to go out or not
9. Product category classification
10. User type classification
11. Sentiment classification in social media: Positive or negative?
12. Document classification (applied in legal search, LinkedIn recommendation, and web search)
Thus data mining field is now dominated by methodology of machine learning. Many people equate data mining to machine learning. This is not surprising given the wide applications we see.