3) What ML algorithms can be used to impute/treat outliers?

Question

Accepted Answer

Various ML algorithms can be used to impute/treat outliers, including k-nearest neighbors, decision trees, and support vector machines.

K-nearest neighbors can be used to impute missing values by finding the k closest data points and using their values to fill in the missing data.
Decision trees can be used to identify and remove outliers by creating a tree that splits the data into groups based on their values.
Support vector machines can be used to identify outliers by finding the data points that are farthest from the decision boundary.
Other algorithms that can be used include random forests, clustering algorithms, and principal component analysis.
The choice of algorithm will depend on the specific problem and the nature of the data.
It is important to carefully evaluate the results of any imputation or outlier treatment method to ensure that they are not introducing bias or distorting the data.

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