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Local Outlier Factor for Anomaly Detection

Posted on by MLNerds

Anomaly detection is an important application used across various verticals like healthcare, finance, manufacturing and so on. Local Outlier Factor is a popular density based technique for anomaly detection that does not require prior examples of anomalies. What are Advantages of LOF for Anomaly Detection? One of the main challenges with Anomaly detection is the…

Anomaly Detection Techniques

Posted on by MLNerds

Anomaly detection is an important task with many applications – right from finding outliers in the data to avoid building bad models to applications such as fraud detection. One of the main challenges with anomaly detection is the lack of labeled data to build supervised classifier models. This video gives a brief overview of five…

Z-Score for Outlier Detection

Posted on by MLNerds

Datasets often contain anomalies or outliers whose properties are different from those of the regular data points. Such outliers if not handled could lead to bad models which do not work even for the typical non-outlier points. How do we identify outliers in data? There are many techniques to identify outliers. the Z-score is one…

Where do we use Divide and Conquer in Machine Learning?

Posted on by MLNerds

Algorithms and data structures are used in various instances while building efficient Machine Learning models. In this video, we explore the “Divide and Conquer” technique and look at two examples where it is used in the context of Machine Learning. The first example is that of the “K-Nearest Neighbours” with a Kd-Tree to make searching…