Hi @harry

Try making a cv(4-fold,7-fold) and evaluate the error matrix accordingly.Example code is given below -

```
params = {}
params["objective"] = "binary:logistic"
params["eta"] = 0.01
params["min_child_weight"] = 7
params["subsample"] = 0.7
params["colsample_bytree"] = 0.7
params["scale_pos_weight"] = 0.8
params["silent"] = 0
params["max_depth"] = 4
params["seed"] = 0
params["eval_metric"] = "auc"
plst = list(params.items())
xgtrain = xgb.DMatrix(x_train,label=y_train,missing=-999)
xgtest = xgb.DMatrix(x_test,missing=-999)
num_rounds = 3000
model = xgb.cv(params, xgtrain, num_rounds,nfold=4,metrics={'auc'}, seed = 0)
```

So it will give you your error value for each round number, you can decide by this your optimal number of rounds, where your test cv score is maximum.

Hope this helps.

Regards,

Aayush