Topic: Complex Predictive Models

You have a large ExampleSet with the following metadata:

You also know that:

there are complex interactions between attributes

there were some errors in the data gathering process, but it is difficult to isolate the examples that have incorrect values

you want to optimize predictive power for future data

Without extensive feature engineering, which of the following machine learning methods would be appropriate to model this data set? $($Select one$)$

A$.$

ARIMA

B$.$

Deep Learning

C$.$

Logistic Regression

D$.$

Linear Regression