# Find correlation between continuos predictive features and a continuos target feature

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,
1.27344838192825, 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")

• What you want is not easy and does not really belong on CrossValidated, you will have better luck on stackoverflow if you really need help with your code. You may also want to take a look at the MLR(3) book: mlr3book.mlr-org.com/feature-selection.html Feb 27 at 17:06
• @VincentGuillemot The code is not the problem. I just need a way to do it, the coding part comes later. What type of feature selection can be done here? should I use spearman correlation maybe?
– CORy
Feb 27 at 19:13