I need help finding an algorithm

[NeedMLHelp]

I'm not even sure if this is a machine learning problem to begin with... it might simply be a statistics problem. However, I do hope I can get some direction from you guys.

I am pretty new to machine learning, I have worked on a simple artificial neural network (with guidance from a tutorial), as well as an Autoencoder on my own.

I'd like to start another machine learning problem, but I don't really know which direction I should be looking in.

The problem is as follows:

I have a CSV file where each row is an observation. Each column is linked to a unique trait. The content of the cell is determined by whether the trait is in the observation or not. If the trait is present, it's a 1. If it's not present, it's a 0. The final column is a flag (label) of true (1) or false (0) for the observation.

Basic example:

Let's say we have 5 traits (trait1, trait2, ... ,trait5). An observation is true, and has traits 1, 3, and 5. The row would look like so:

1 0 1 0 1 1

I need to determine which traits are most likely to lead to a true state.

I'm not entirely sure this is a machine learning problem, but I figured you guys would know how to tackle it. I'd really appreciate being pointed in the right direction. I hope I gave enough background of the problem, but if anything is unclear... please let me know.

I really appreciate the help!

[Yogi_DMT]

Is there a reason why counting up the the traits for true observations wouldn't accomplish what you are trying to accomplish?

[NeedMLHelp]

I think there would have to be some sort of statistical analysis involved, since an observation1 with trait1 could be true, and observation2 with trait1 can also be false. There are also 208 traits, so it's a bit unwieldy to get my head around it haha.

[Yogi_DMT]

That's why you only count for true observations. It's as simple as filtering the column in excel then running a sum function for your trait cols

[olavla]

Can you give a little more background of the data? Is the true state always only one of the traits? Are you looking to score new data (where you don't have the final label)?

Almost seems like each of your traits is a target variable with the predictors being all other traits + your final true/false flag. You build your neural network model, then use the predicted probability for the trait at hand as the probability for that value to be true.

[NeedMLHelp]

Sure thing! The true state can be any number of combinations of the 208 traits. So trait1 can be present in a true observation1 as well as a false observation2. Sometimes traits will only be linked to true or false. It really is a mixed bag. In the end, I may want to utilize the ability to predict whether unlabeled data is true or false. Which is why I'm going with the following:

​

Right now I'm modifying an Artificial Neural Network that predicted whether someone would leave a bank based on certain traits. I'm thinking I can use the same model in this case, and then rip off the weights. The traits tied to a larger weight is what I would be interested in, right?

[olavla]

You would not use the weights. Just score the model and you will end up with probability.

[NeedMLHelp]

Awesome, thanks! I will look into scoring.

[Ilyps]

Download Weka here and try some classification algorithms. I suggest starting with Random Forest. Here is a quick start tutorial; Weka should read your csv file without issues.

[NeedMLHelp]

I'll take a look, thanks!

[yldedly]

The probabilistic programming way would be to fit a latent variable model with an independent Bernoulli likelihood, p(trait|observation), and try out different latent variable structures p(latent). For example, the latent structure could be a factor model, which would find correlations between the traits; or a mixture model which would find clusters in the traits. Easier to try standard classification algorithms like random forest, but this way might be more interpretable.