Vikash B - 4 months ago 48

R Question

I am trying out the Kaggle housing prices challenge : https://www.kaggle.com/c/house-prices-advanced-regression-techniques

Here is the script I wrote

`train <- read.csv("train.csv")`

train$Id <- NULL

previous_na_action = options('na.action')

options(na.action = 'na.pass')

sparse_matrix <- sparse.model.matrix(SalePrice~.-1,data = train)

options(na.action = previous_na_action)

model <- xgboost(data = sparse_matrix, label = train$SalePrice, missing = NA, max.depth = 6, eta = 0.3, nthread = 4, nrounds = 16, verbose = 2, objective = "reg:linear")

importance <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = model)

print(xgb.plot.importance(importance_matrix = importance))

The data has over 70 features, I used

`xgboost`

`max.depth`

`nrounds`

The importance plot i am getting is very messed up, how do i get to view only the top 5 features or something.

Answer

Check out the `top_n`

argument to `xgb.plot.importance`

. It does exactly what you want.

```
# Plot only top 5 most important variables.
print(xgb.plot.importance(importance_matrix = importance, top_n = 5))
```

Edit: only on development version of xgboost. Alternative method is to do this:

```
print(xgb.plot.importance(importance_matrix = importance[1:5]))
```