using a trained boosting model in matlab

Hi,

I have a boosting regression model that has been trained using python, and I want to read the model in Matlab and use it for prediction. I don't have the deep learning toolbox and for this specific task, I can not call python through Matlab.

a trained model looks something like this (I removed feature names, feature info and the actual number of trees for readability):

tree

version=v2

num_class=1

num_tree_per_iteration=1

label_index=0

max_feature_idx=43

objective=regression_l1

feature_names=feature_1 ... feature_M

feature_infos=[feature_1_min:feature_1_max]...[feature_M_min:feature_M_max]

tree_sizes=tree_0_size ... tree_N_size

Tree=0

num_leaves=4

num_cat=0

split_feature=8 41 6

split_gain=498454 42849.9 39363.4

threshold=-9.4999999999999982 0.80130323767662059 -93.099506378173814

decision_type=2 2 2

left_child=2 -2 -1

right_child=1 -3 -4

leaf_value=-0.22684870120137932 -0.0063567292317748061 0.12475228909403085 -0.094960116110742093

leaf_count=361156 218908 351085 222136

internal_value=0 0.665046 -0.649885

internal_count=1153285 569993 583292

shrinkage=1

Tree=1

num_leaves=4

num_cat=0

split_feature=9 42 4

split_gain=476541 42391.7 40019.2

threshold=10.371099948883058 -0.85092586278915394 1.2349531054496767

decision_type=2 2 2

left_child=1 -1 -2

right_child=2 -3 -4

leaf_value=0.17211716407500208 -0.032555124533548953 0.044790033876523375 -0.16342578789032997

leaf_count=336891 165211 235997 415186

internal_value=0 0.646403 -0.639241

internal_count=1153285 572888 580397

shrinkage=0.01

...

Tree=N

num_leaves=4

num_cat=0

split_feature=8 9 41

split_gain=484566 43234.8 38827.9

threshold=-4.4999999999999991 89.511920928955092 0.84224075078964245

decision_type=2 2 2

left_child=1 -1 -2

right_child=2 -3 -4

leaf_value=-0.039807440089253521 0.045218650022099877 -0.17164923107102309 0.16946350076781358

leaf_count=226080 213732 370436 343037

internal_value=0 -0.627055 0.670112

internal_count=1153285 596516 556769

shrinkage=0.01

anny suggestion on how to read the model and use it is greatly appriciated