How to params to XGboost while using it in mlforecast

[sankara-nps]

import xgboost as xgb

Assuming you have your features and target variable ready (X and y)

Combine them into a DMatrix

dtrain = xgb.DMatrix(X, label=y)

Define XGBoost parameters with Pseudo-Huber loss

params = {
'objective': 'reg:pseudohubererror', # Specify 'reg:pseudohubererror' for Pseudo-Huber loss
'alpha': 1.2, # Adjust this parameter to control the Pseudo-Huber delta
'eta': 0.1, # Learning rate
'max_depth': 6, # Maximum depth of the tree
'subsample': 0.8, # Fraction of data used for building trees
'colsample_bytree': 0.8, # Fraction of features used for building trees
'eval_metric': 'rmse' # Evaluation metric (can be 'rmse' or other appropriate metrics)
}

Train the XGBoost model using the entire dataset

num_round = 100 # Number of boosting rounds (you can adjust this)
model = xgb.train(params, dtrain, num_round)

Make predictions on the same data used for training (since you have no separate test set)

predictions = model.predict(dtrain)

predictions now contain the predicted values for your entire dataset

I want do something like this, as we are passing the model directly , I am not sure how to pass dmatrix. Could someone pls give suggestions

[jmoralez]

Closing in favor of #219