I am trying to run this julia script The goal consists in finding best parameters tuning for a XGBoost Regressor model

using MLJ, MLJXGBoostInterface, MLJTuning, Random, XGBoost

# Load the XGBoost Regressor model
XGBoostRegressor = @load XGBoostRegressor 

# Generate synthetic regression dataset
X, y = MLJ.make_regression(200, 5; noise=0.3, rng=1234)
train, test = partition(eachindex(y), 0.8, shuffle=true, rng=1234)

# Define the base model with placeholders for hyperparameters
model = XGBoostRegressor(
    objective="reg:squarederror",
    eta=0.1,        # Learning rate
    max_depth=6,    # Tree depth
    num_round=100,  # Boosting rounds
)

# Define the hyperparameter tuning ranges
tuned_model = TunedModel(
    model=model,
    tuning=RandomSearch(),  
    resampling=CV(nfolds=5, shuffle=true, rng=1234),  
    range=[
        :eta => range(0.01, 0.3, length=5),
        :max_depth => 2:10,
        :num_round => range(50, 200, length=4)
    ],
    measure=rms,  
    n=10,  
    acceleration=CPUThreads()
);


# Wrap the model into a machine
mach = machine(tuned_model, X, y);

# Train the tuned model
fit!(mach, rows=train);

But at this point julia returns:

┌ Error: Problem fitting the machine machine(DeterministicTunedModel(model = XGBoostRegressor(test = 1, …), …), …). 
└ @ MLJBase ~/.julia/packages/MLJBase/7nGJF/src/machines.jl:694
[ Info: Running type checks... 
[ Info: Type checks okay. 
ERROR: ArgumentError: Unsupported range #2. 

Does anyone have an idea about what is causing this error?

I am trying to run this julia script The goal consists in finding best parameters tuning for a XGBoost Regressor model

using MLJ, MLJXGBoostInterface, MLJTuning, Random, XGBoost

# Load the XGBoost Regressor model
XGBoostRegressor = @load XGBoostRegressor 

# Generate synthetic regression dataset
X, y = MLJ.make_regression(200, 5; noise=0.3, rng=1234)
train, test = partition(eachindex(y), 0.8, shuffle=true, rng=1234)

# Define the base model with placeholders for hyperparameters
model = XGBoostRegressor(
    objective="reg:squarederror",
    eta=0.1,        # Learning rate
    max_depth=6,    # Tree depth
    num_round=100,  # Boosting rounds
)

# Define the hyperparameter tuning ranges
tuned_model = TunedModel(
    model=model,
    tuning=RandomSearch(),  
    resampling=CV(nfolds=5, shuffle=true, rng=1234),  
    range=[
        :eta => range(0.01, 0.3, length=5),
        :max_depth => 2:10,
        :num_round => range(50, 200, length=4)
    ],
    measure=rms,  
    n=10,  
    acceleration=CPUThreads()
);


# Wrap the model into a machine
mach = machine(tuned_model, X, y);

# Train the tuned model
fit!(mach, rows=train);

But at this point julia returns:

┌ Error: Problem fitting the machine machine(DeterministicTunedModel(model = XGBoostRegressor(test = 1, …), …), …). 
└ @ MLJBase ~/.julia/packages/MLJBase/7nGJF/src/machines.jl:694
[ Info: Running type checks... 
[ Info: Type checks okay. 
ERROR: ArgumentError: Unsupported range #2. 

Does anyone have an idea about what is causing this error?

• julia

1 Answer 1

For a field sampler "pair of the form (field, s)"

• use Tuple syntax, like (:fieldname, s)
• not Pair syntax, like :fieldname => s

MLJTuning.RandomSearch