Can you use machine learning for data with binary outcomes?

Question

I am totally new to artificial intelligence and neural networks and have a broad question that I hope is appropriate to ask here.

I am an ecologist working in animal movement and I want to use AI to apply to my field. This will be one of the few times this has been attempted so there is not much literature to help me here.

My dataset is binary. In short, I have the presence (1) and absence (0) of animal locations that are associated with a series of covariates (~20 environmental conditions such as temperature, etc.). I have ~1 million rows of data to train the model on with a ratio of 1:100 (presence:absence).

Once trained, I would like a model that can predict if an animal will be in a location (or give a probability) based on new covariates (environmental conditions).

Is this sort of thing possible using AI?

(If so, where should I be looking for resources? I write in R, should I learn Python?)

Answer 1

Of course, you can use AI (especially Deep Learning) in your application. Your covariates will be the input to your AI model and the model should predict the probability of presence. The model has no problem with binary data and binary data is common in this field.

Also, note that the 1:100 ratio is not good and the network will probably learn to output absence for any input (this way it gets 99% accuracy but really it's not doing anything). So, you should probably balance them (using almost the same data, or telling the network to pay more attention to presence data (by weighting the related loss)).

I think nowadays you can find Deep Learning in any popular coding language. But most of the DL community uses Python and it's really easy to learn. If you want to learn Deep Learning there are a lot of sources on the internet. But I suggest you the Deep Learning courses of Deeplearning.ai in Coursera (If you have a lot of time) and CS231n of Stanford university on youtube (If you have time).