Yes, we can do that. Given that a model is adequately regularised this should not be a grave issue. Ideally we always want to avoid providing redundant explanatory features $x$ to our classifiers but most classifiers can handle such a situation reasonably well.
Conceptually, this "double use" of a feature will be akin to having the same signal ...
The boruta algorithm is a feature selection method that is based on random forest. The reason you might care is that lasso is a linear model, so anything that matters for your outcome that isn't linear in the parameters under estimation is at risk of getting eliminated. An example: What are disadvantages of using the lasso for variable selection for ...
Good publication (still under review) for combining multiple predictors is:
Optimal blending of multiple independent prediction models
It contains formulas for combining the models based on their variances as well as formula for the variance of the ...
Given that category 1 only accounts for 7.5% of your sample - then yes, your sample is highly imbalanced.
Look at the recall score for category 1 - it is a score of 0. This means that of the entries for category 1 in your sample, the model does not identify any of these correctly.
The high f-score accuracy of 86% is misleading in this case. It means that ...
There are clearly lots of ways you could attack this, and @Sextus Empiricus lists some good ones. From a slightly more practical point of view, I would think about doing the following.
Since each player plays only once, it's safe enough to just calculate change scores for each player.
Clustering by team is clearly a sensible thing to do, but it makes it ...
Purely as an initial explanatory investigation I would use some plots of effect-size vs moderator.
Using clustering or other reduction of dimensionality
Potentially you might perform some cluster analysis on your population based on the other variables. Then after classifying your users into groups, or by expressing the group membership with some ...
Just run this method varimp() :
print("variable relative_importance scaled_importance percentage")
for variable,relative_importance,scaled_importance,percentagein modelh2o.varimp():