Logic to train with LightGBM
lgb.train( params = list(), data, nrounds = 10L, valids = list(), obj = NULL, eval = NULL, verbose = 1L, record = TRUE, eval_freq = 1L, init_model = NULL, colnames = NULL, categorical_feature = NULL, early_stopping_rounds = NULL, callbacks = list(), reset_data = FALSE, ... )
params | List of parameters |
---|---|
data | a |
nrounds | number of training rounds |
valids | a list of |
obj | objective function, can be character or custom objective function. Examples include
|
eval | evaluation function, can be (a list of) character or custom eval function |
verbose | verbosity for output, if <= 0, also will disable the print of evaluation during training |
record | Boolean, TRUE will record iteration message to |
eval_freq | evaluation output frequency, only effect when verbose > 0 |
init_model | path of model file of |
colnames | feature names, if not null, will use this to overwrite the names in dataset |
categorical_feature | list of str or int type int represents index, type str represents feature names |
early_stopping_rounds | int. Activates early stopping. Requires at least one validation data and one metric. If there's more than one, will check all of them except the training data. Returns the model with (best_iter + early_stopping_rounds). If early stopping occurs, the model will have 'best_iter' field. |
callbacks | List of callback functions that are applied at each iteration. |
reset_data | Boolean, setting it to TRUE (not the default value) will transform the booster model into a predictor model which frees up memory and the original datasets |
... | other parameters, see Parameters.rst for more information. A few key parameters:
|
a trained booster model lgb.Booster
.
# \dontrun{ data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) params <- list(objective = "regression", metric = "l2") valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 5L , valids = valids , min_data = 1L , learning_rate = 1.0 , early_stopping_rounds = 3L )#> [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001432 seconds. #> You can set `force_row_wise=true` to remove the overhead. #> And if memory is not enough, you can set `force_col_wise=true`. #> [LightGBM] [Info] Total Bins 232 #> [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116 #> [LightGBM] [Info] Start training from score 0.482113 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [1]: test's l2:6.44165e-17 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [2]: test's l2:1.97215e-31 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [3]: test's l2:0 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements #> [4]: test's l2:0 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements #> [5]: test's l2:0# }