Using Isolation Forest to find outliers

Isolation Forest is a relatively new machine learning technique for identifying anomalies. It has quickly become popular, partly because its algorithm is optimized to find outliers, rather than normal values. It finds outliers by successively partitioning the data until a data point has been isolated. Points that require fewer partitions to be isolated receive higher anomaly scores. This process turns out to be fairly easy on system resources. In this section, we will learn how to use it to detect outlier COVID-19 cases and deaths.

Isolation Forest is a good alternative to KNN, particularly when we're working with large datasets. The efficiency of the algorithm allows it to handle large samples and a high number of features. Let's get started:

1. We can do an analysis similar to the one in the previous section with Isolation Forest rather than KNN. Let's start by loading scikit-learn's StandardScaler and IsolationForest...