Using MOSTLY AI Generators as Universal Prediction Models

In this video, Matthias from MOSTLY AI presents a detailed tutorial on using MOSTLY AI Generators to predict missing columns in a dataset. Instead of the typical generation of synthetic data, Matthias demonstrates a unique approach by using the generator for universal prediction.

Using the US Census data, Matthias explains the process of splitting the data into training and test sets. He then trains a Generator on the training set and uses it to predict missing values in the test set. Specifically, he focuses on predicting the "marital status" and "race" columns by dropping these columns from the test data and using the generator to synthesize the missing values.

Matthias walks through each step, from loading the dataset to training the Generator and generating the synthetic data. He evaluates the performance, achieving a respectable 79% accuracy for both columns. He also compares this method with traditional machine learning models, highlighting the convenience and versatility of the MOSTLY AI Platform, despite the slightly higher accuracy of dedicated models.