In my data, I have about 10K predictive features (genes), and one target feature (age). I want to predict the ages according to the genes. The rows in the data are the patients. To do so I plan to use Regression Random Forest.
I don't want to use this many predictive features, so I want to do some feature selection first.
There is no linear correlation between the predictive features and the target feature (at least I didn't find any relationship for the features that I checked).
For binary features, when I want to predict gender for example, I can just use wilcoxon test to find the most significant features that separate the two classes. Here, I can't use such a test.
How can I find the most important features for age prediction? Can I just run a random forest algorithm and then just check the most important feature? would that work without creating noise?
Here is a subset of the training set, including the age feature:
dput(train_scaled[1:20,c(1,2,3,4,5,dim(train_scaled)[2])])
structure(list(A1BG = c(1.81619824260442, 1.9986779809134, 1.91171736562985,
1.87425799530611, 1.95720931978885, 1.68534041055052, 1.89237252718096,
1.67216783026329, 1.94555622783709, 2.05581255682001, 1.89803035420513,
1.7563466972377, 1.85448031100116, 1.90469081497093, 1.82958626152702,
1.80639351405546, 1.94904037078298, 1.88121448353727, 1.90265126862802,
1.89685997844076), ADA = c(1.25649644487907, 1.22729343759834,
1.27344838192825, 1.25955928072103, 1.26370991138808, 1.20355435132166,
1.23956642505305, 1.25589256673664, 1.15163992141014, 1.20146398841983,
1.09375020345131, 1.19284479092003, 1.18821270400345, 1.15707902340534,
1.29848225592125, 1.2563306911831, 1.29923301554395, 1.22251152311355,
1.22795303612616, 1.48761789143517), CDH2 = c(0.53688090267567,
0.493919738045297, 0.560208693940622, 0.588029409349587, 0.559643640625794,
0.570599153392745, 0.562110779919758, 0.54921119370662, 0.507086211915313,
0.496614809627379, 0.581539495325737, 0.597444486905757, 0.560166965896316,
0.579972731871132, 0.583039148505923, 0.581924465154048, 0.566420208700464,
0.576395012253254, 0.575907185558433, 0.453946904680819), AKT3 = c(0.917211707537678,
0.892003590486357, 0.969818024729793, 0.978292068213014, 0.913032018184228,
0.948312269441081, 0.947709935054217, 0.83611701240751, 0.912172816373717,
0.98719118237761, 1.02711099335984, 0.922819275258826, 0.933697725060485,
0.996194969362905, 0.971300509819334, 0.851048415219854, 0.9156277536571,
0.982369058418409, 0.832254764434006, 0.905941809264712), MED6 = c(2.02291559929045,
2.08170269351807, 2.04355176601994, 2.05526765226102, 1.93189920401206,
2.03859461894252, 1.97348257053102, 1.9229558498545, 1.95605272086482,
2.06298256427372, 2.11184798077237, 1.99810309844712, 2.01005618200693,
2.06589538426559, 2.1372244020894, 1.967894127866, 2.01416144921981,
2.02184221220218, 1.90343367987094, 1.9634446015096), age = c(69,
30, 64, 65, 61, 70, 48, 73, 40, 58, 62, 53, 75, 68, 52, 67, 50,
70, 78, 53)), row.names = c("Patient12", "P10", "P11",
"PX123", "PX77", "P1", "ER45", "ER30", "Patient8",
"Patient9", "Patient10", "EA6327611", "EA6329802", "EA6839018", "EA6389069",
"EA6359107", "EA6359120", "EA6391391", "EA6399146", "EA6391153"), class = "data.frame")
