What kind of ML model can find missing parameters?

Question

Given a data set, such as:

(FirstName, LastName, Sex, DateOfBirth, HairColor, EyeColor, Height, Weight, Location)

that some model can train on, what kind of Machine Learning paradigm can be used to predict missing values if only given some of them?

Example:

Given:

(FirstName: John, LastName: Doe, Sex: M, Height: (5,10))

What model could predict the missing values?

(DateOfBirth, HairColor, EyeColor, Weight, Location)

In other words, the model should be able to take any of the fields as inputs, and "fill in" any that are missing.

And what type of ML/DL is this even called?

Answer 1

If you're looking to fill missing values with an algorithm, this is called imputing missing data. If you're using Python, the scikit-learn library has a number of imputation algorithms that you can explore in the docs.

One nice algorithm is KNNImputer, which looks n_neighbors most similar observations to the current observation and fills the missing data with mean for the column from those similar observations. Read more here: https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html