Python implementation of LambdaMART
LambdaMART(**kwargs)
Parameters:
kwargs: XGBRegressor parameters
default :
(max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, objective='reg:squarederror', booster='gbtree',
tree_method='auto', n_jobs=1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1,
colsample_bylevel=1, colsample_bynode=1, reg_alpha=0,reg_lambda=1,scale_pos_weight=1,base_score=0.5,random_state=0,
missing=None, num_parallel_tree=1, importance_type='gain', **kwargs)
Methods:
fit: Fits the model on the training data.
Parameters: None
Returns: None
predict: Predicts the scores for the test dataset.
Parameters: Numpy array of documents with each document’s format is [query index, feature vector]
Returns: Numpy array of scores
save: Saves the model into file with the name given as a parameter
Parameters: Filename
Returns: None
load: Loads the model from the file given as a parameter
Parameters: Filename
Returns: None