I was provided with a data file which looks like this:
f1,f2,f3,f4,lbl1,lbl2,lbl3 -99.78507814566231,0.9487271429068756,6.354574478964145,-0.92299976023887444,-1.0,7.785451430712673,category2 -100.45507785716314,2.780304246171124,3,131265316135844,2.43832325135605543,-1.0,5.229512156937786,category3 -80.35411219282111,3.31425742741509244,4.12266026421345,15.422132963016145,1.0,9.45646860327132,category1 [...]
There are four columns of features and the last three columns are labels that I need to predict.
The exercise involves the following questions:
- We suspect that there's correlation between the 3 labels. Investigate that correlation.
- Can the task be solved by making only one algorithm that predicts one of the label values, after which the other labels can be deduced?
- If so, which label should be predicted by an algorithm and which can be deduced from it?
- What type of supervised learning would be suitable for predicting the selected label column and why?
I feel kinda lost because labels seems to be of mixed type: two of them are floating point values, the last one - categorical.
- How can I look into correlation,
- How do I pick just one label
- What supervised learning would be suitable to make predictions here?