Hack-R - 1 year ago 324

R Question

I'm asking this in reference to the R library

`lightgbm`

There are 3 parameters wherein you can choose statistics of interest for your model -

`metric`

`eval`

`obj`

The documentation says:

objobjective function, can be character or custom objective function. Examples include regression, regression_l1, huber, binary,

lambdarank, multiclass, multiclass

evalevaluation function, can be (list of) character or custom eval function

metric, default={l2 for regression}, {binary_logloss for binary

classification},{ndcg for lambdarank}, type=multi-enum,

options=l1,l2,ndcg,auc,binary_logloss,binary_error...

l1, absolute loss, alias=mean_absolute_error, mae

l2, square loss, alias=mean_squared_error, mse

l2_root, root square loss, alias=root_mean_squared_error, rmse

huber, Huber loss

fair, Fair loss

poisson, Poisson regression

ndcg, NDCG

map, MAP

auc, AUC

binary_logloss, log loss

binary_error. For one sample 0 for correct classification, 1 for error classification.

multi_logloss, log loss for mulit-class classification

multi_error. error rate for mulit-class classification

Support multi metrics, separate by , metric_freq, default=1, type=int

frequency for metric output is_training_metric, default=false, type=bool

set this to true if need to output metric result of training ndcg_at, default=1,2,3,4,5, type=multi-int, alias=ndcg_eval_at,eval_at

NDCG evaluation position, separate by ,

My best guess is that

- is the objective function of the algorithm, i.e. what it's trying to maximize or minimize, e.g. "regression" means it's minimizing squared residuals
`obj`

- I'm guessing is just one or more additional statistics you'd like to see computed as your algorithm is being fit.
`eval`

- I have no clue how this is used differently than
`metric`

and`obj`

`eval`

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Answer Source

As you have said,

obj is the objective function of the algorithm, i.e. what it's trying to maximize or minimize, e.g. "regression" means it's minimizing squared residuals.

Metric and eval are essentially the same. They only really differ in where they are used. Eval is used with the cross-validation method (because it can be used to evaluate the model for early-stopping etc?). Metric is used in the normal train situation.

The confusion arises from the influence on several gbm variants (xgboost, lightgbm and sklearn's gbm + maybe an R package) all having slightly differing argument names. For example xgb.cv() in python uses `eval`

but for R it uses `metric`

. Then in lgbm.cv() for python and R `eval`

is used.

I have been very confused switching between xgboost and lightgbm. There is an absolutely amazing resource by Laurae that helps you understand each parameter.

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