# nlambdas¶

• Available in: GLM
• Hyperparameter: no

## Description¶

This option specifies the number of lambdas to use during a lambda search. As such, this option is only available if lambda_search=TRUE.

nlambdas works in conjunction with lambda_min_ratio. The sequence of the $$\lambda$$ values is automatically generated as an exponentially decreasing sequence. It ranges from $$\lambda_{max}$$ (the smallest $$\lambda$$ so that the solution is a model with all 0s) to $$\lambda_{min} =$$ lambda_min_ratio $$\times$$ $$\lambda_{max}$$.

H2O computes $$\lambda$$ models sequentially and in decreasing order, warm-starting the model (using the previous solution as the initial prediction) for $$\lambda_k$$ with the solution for $$\lambda_{k-1}$$. By warm-starting the models, we get better performance. Typically models for subsequent $$\lambda$$ values are close to each other, so only a few iterations per $$\lambda$$ are needed (two or three). This also achieves greater numerical stability because models with a higher penalty are easier to compute. This method starts with an easy problem and then continues to make small adjustments.

Notes:

• lambda_min_ratio and nlambdas also specify the relative distance of any two lambdas in the sequence. This is important when applying recursive strong rules, which are only effective if the neighboring lambdas are “close” to each other.
• When alpha > 0, the default value for lambda_min_ratio is $$1e^{-4}$$, and the default value for nlambdas is 100. This gives a ratio of 0.912. For best results when using strong rules, keep the ratio close to this default.
• When alpha=0, the default value for nlamdas is set to 30 because fewer lambdas are needed for ridge regression.

## Example¶

library(h2o)
h2o.init()

# import the boston dataset:
# this dataset looks at features of the boston suburbs and predicts median housing prices
# the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Housing
boston <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")

# set the predictor names and the response column name
predictors <- colnames(boston)[1:13]
# set the response column to "medv", the median value of owner-occupied homes in $1000's response <- "medv" # convert the chas column to a factor (chas = Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)) boston["chas"] <- as.factor(boston["chas"]) # split into train and validation sets boston.splits <- h2o.splitFrame(data = boston, ratios = .8) train <- boston.splits[[1]] valid <- boston.splits[[2]] # try using the nlambas parameter: # train your model boston_glm <- h2o.glm(x = predictors, y = response, training_frame = train, validation_frame = valid, lambda_search = TRUE, nlambdas = 50) # print the mse for the validation data print(h2o.mse(boston_glm, valid=TRUE))  import h2o from h2o.estimators.glm import H2OGeneralizedLinearEstimator h2o.init() # import the boston dataset: # this dataset looks at features of the boston suburbs and predicts median housing prices # the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Housing boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") # set the predictor names and the response column name predictors = boston.columns[:-1] # set the response column to "medv", the median value of owner-occupied homes in$1000's
response = "medv"

# convert the chas column to a factor (chas = Charles River dummy variable (= 1 if tract bounds river; 0 otherwise))
boston['chas'] = boston['chas'].asfactor()

# split into train and validation sets
train, valid = boston.split_frame(ratios = [.8])

# try using the nlambdas parameter:
# initialize the estimator then train the model
boston_glm = H2OGeneralizedLinearEstimator(lambda_search = True, nlambdas = 50)
boston_glm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

# print the mse for the validation data
print(boston_glm.mse(valid=True))