`weights_column`

¶

Available in: GBM, DRF, Deep Learning, GLM, GAM, AutoML, XGBoost, CoxPH

Hyperparameter: no

## Description¶

This option specifies the column in a training frame to be used when determining weights. Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are also supported. During training, rows with higher weights matter more, due to the larger loss function pre-factor.

For scoring, all computed metrics will take the observation weights into account (for Gains/Lift, AUC, confusion matrices, logloss, etc.), so it’s important to also provide the weights column for validation or test sets if you want to up/down-weight certain observations (ideally consistently between training and testing). Note that if you omit the weights, then all observations will have equal weights (set to 1) for the computation of the metrics. For example, a weight of 2 is identical to duplicating a row.

**Notes**:

Weights can be specified as integers or as non-integers.

The weights column cannot be the same as the fold_column.

If a weights column is specified as both a feature (predictor) and a weight, the column will be used for weights only.

Example unit test scripts are available on GitHub:

### Observation Weights in Deep Learning¶

The observation weights are handled differently in Deep Learning than in the other supported algorithms. For algorithms other than Deep Learning, the weight goes into the split-finding and leaf-node prediction math in a straightforward way. For Deep Learning, it’s more difficult. Using the weight as a multiplicative factor in the loss will not work in general, and that would not be the same as replicating the same row. Also, applying the same row over and over isn’t a good idea either, so sampling during training should still be active. To address these issues, Deep Learning is implemented with importance sampling using the inverse cumulative distribution function. It also includes a special case that picks a random row from the dataset for every second row it trains, just to keep outliers in the game. Note that observation weights for Deep Learning that are neither 0 nor 1 are difficult to handle properly. In this case, it might be better to explicitly oversample using `balance_classes=TRUE`

.

## Example¶

```
library(h2o)
h2o.init()
# import the cars dataset:
# this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made
cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
# convert response column to a factor
cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"])
# set the predictor names and the response column name
predictors <- c("displacement", "power", "acceleration", "year")
response <- "economy_20mpg"
# create a new column that specifies the weights
# or use a column that already exists in the dataframe
# Note: do not use the fold_column
# in this case we will use the "weight" column that already exists in the dataframe
# this column contains the integers 1 or 2 in each row
# split into train and validation sets
cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234)
train <- cars_split[[1]]
valid <- cars_split[[2]]
# try using the `weights_column` parameter:
# train your model, where you specify the weights_column
cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
validation_frame = valid, weights_column = "weight", seed = 1234)
# print the auc for the validation data
print(h2o.auc(cars_gbm, valid = TRUE))
```

```
import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
h2o.init()
h2o.cluster().show_status()
# import the cars dataset:
# this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made
cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
# convert response column to a factor
cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
# set the predictor names and the response column name
predictors = ["displacement","power","acceleration","year"]
response = "economy_20mpg"
# create a new column that specifies the weights
# or use a column that already exists in the dataframe
# Note: do not use the fold_column
# in this case we will use the "weight" column that already exists in the dataframe
# this column contains the integers 1 or 2 in each row
# split into train and validation sets
train, valid = cars.split_frame(ratios = [.8], seed = 1234)
# try using the `weights_column` parameter:
# first initialize your estimator
cars_gbm = H2OGradientBoostingEstimator(seed = 1234)
# then train your model, where you specify the weights_column
cars_gbm.train(x = predictors, y = response, training_frame = train,
validation_frame = valid, weights_column = "weight")
# print the auc for the validation data
cars_gbm.auc(valid=True)
```