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library(lightgbm)
# We load the default iris dataset shipped with R
data(iris)
# We must convert factors to numeric
# They must be starting from number 0 to use multiclass
# For instance: 0, 1, 2, 3, 4, 5...
iris$Species <- as.numeric(as.factor(iris$Species)) - 1L
# We cut the data set into 80% train and 20% validation
# The 10 last samples of each class are for validation
train <- as.matrix(iris[c(1L:40L, 51L:90L, 101L:140L), ])
test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ])
dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L])
dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L])
valids <- list(test = dtest)
# Method 1 of training
params <- list(
objective = "multiclass"
, metric = "multi_error"
, num_class = 3L
, min_data = 1L
, learning_rate = 1.0
)
model <- lgb.train(
params
, dtrain
, 100L
, valids
, early_stopping_rounds = 10L
)
# We can predict on test data, outputs a 90-length vector
# Order: obs1 class1, obs1 class2, obs1 class3, obs2 class1, obs2 class2, obs2 class3...
my_preds <- predict(model, test[, 1L:4L])
# Method 2 of training, identical
params <- list(
min_data = 1L
, learning_rate = 1.0
, objective = "multiclass"
, metric = "multi_error"
, num_class = 3L
)
model <- lgb.train(
params
, dtrain
, 100L
, valids
, early_stopping_rounds = 10L
)
# We can predict on test data, identical
my_preds <- predict(model, test[, 1L:4L])
# A (30x3) matrix with the predictions
# class1 class2 class3
# obs1 obs1 obs1
# obs2 obs2 obs2
# .... .... ....
my_preds <- predict(model, test[, 1L:4L])
# We can also get the predicted scores before the Sigmoid/Softmax application
my_preds <- predict(model, test[, 1L:4L], type = "raw")
# We can also get the leaf index
my_preds <- predict(model, test[, 1L:4L], type = "leaf")
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