Machine Learning technique that helps in detecting the outliers in data.2 pointsClusteringClassificationAnomaly DetectionAll of the above

The Machine Learning technique that helps in detecting the outliers in data is Anomaly Detection.

Here's why:

1. Clustering: This is a technique used to group similar data points together, which can help in understanding the structure and patterns in a dataset. However, it's not specifically designed for outlier detection.

2. Classification: This is a supervised learning technique used to predict the class or category of a given data point. While it can be used to identify unusual data if you have labeled examples of outliers, it's not primarily used for outlier detection.

3. Anomaly Detection: This technique is specifically designed to identify unusual data points or outliers. It works by learning the 'normal' patterns in the data and then identifying any points that deviate from this norm as potential outliers.

4. All of the above: While all these techniques can be used in the broader data analysis process, Anomaly Detection is the most direct and specific method for detecting outliers in data.