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El Potaeto 2015-01-01 13:50:28 +01:00
parent 901904b535
commit 5e5500d6d3
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R-package/demo/create_sparse_matrix.R
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@ -70,7 +70,7 @@ xgb.dump(bst, 'xgb.model.dump', with.stats = T)
# sparse_matrix@Dimnames[[2]] represents the column names of the sparse matrix. # sparse_matrix@Dimnames[[2]] represents the column names of the sparse matrix.
importance <- xgb.importance(sparse_matrix@Dimnames[[2]], 'xgb.model.dump') importance <- xgb.importance(sparse_matrix@Dimnames[[2]], 'xgb.model.dump')
print(importance) print(importance)
# According to the matrix below, the most important feature in this dataset to predict if the treatment will work is the Age. The second most important feature is having received a placebo or not. The sex is third. Then we see our generated features (AgeDiscret). We can see that there contribution is very low. # According to the matrix below, the most important feature in this dataset to predict if the treatment will work is the Age. The second most important feature is having received a placebo or not. The sex is third. Then we see our generated features (AgeDiscret). We can see that their contribution is very low (Gain column).
# Does these results make sense? # Does these results make sense?
# Let's check some Chi2 between each of these features and the outcome. # Let's check some Chi2 between each of these features and the outcome.