Match the real-world problems below with the corresponding Machine Learning terms.
Hint
Let's revisit the definitions from the theory and define an additional term:
1. If the label can take on just a few distinct values, the problem is referred to as classification.
2. A typical example of an unsupervised ML algorithm is clustering. Its goal is to group examples from the data into so-called clusters, or groups, based on how similar they are.
3. If the label [we are trying to predict] is numerical, the problem is referred to as regression.
4. ML algorithms are at the core of many security systems, for example, credit card fraud detection, which falls under the broader category of anomaly detection.
5. Time-series forecasting is the process of using historical data to predict future values or patterns in a sequence of data points collected over time.