I have data with a minimum number of features that don't change, and a few additional features that can change and have a big impact on the outcome. My data-set looks like this:
Features are A, B, C (always present), and D, E, F, G, H (sometimes present)
A = 10, B = 10, C = 10 outcome = 10 A = 8, B = 7, C = 8 outcome = 8.5 A = 10, B = 5, C = 11, D = 15 outcome = 178 A = 10, B = 10, C = 10, E = 10, G = 18 outcome = 19 A = 10, B = 8, C = 9, E = 8, F = 4 outcome = 250 A = 10, B = 11, C = 13, E = 8, F = 4 outcome = 320 ...
I want to predict the outcome value, and the combination of additional parameters is very important for determining the outcome. In this example, the presence of E and F leads to a big outcome, whereas the presence of E and G doesn't. What machine learning algorithms or techniques are good to capture this phenomenon ?