issues
search
fabsig
/
KTBoost
A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.
Other
60
stars
19
forks
source link
issues
Newest
Newest
Most commented
Recently updated
Oldest
Least commented
Least recently updated
Is this library still maintained?
#14
ogencoglu
closed
1 month ago
1
Multiprocessing with KTBoost
#13
astrogilda
opened
2 years ago
0
KTBoost.BoostingRegressor TypeError: __cinit__() takes exactly 6 positional arguments (7 given)
#12
fiwe1601
closed
2 years ago
1
Tobit with yl and yu varied by observations
#11
kota7
opened
3 years ago
1
Adding tweedie loss
#10
davidlkl
closed
3 years ago
7
Compatibility with scikit-learn 0.24.0
#9
davidlkl
closed
3 years ago
4
built KTBoost conda package
#8
BaseconDev
closed
4 years ago
0
Is it possible to add a monotone constraint?
#7
flippercy
opened
4 years ago
1
sample_weight is being multiplied twice - Tobit Loss
#6
sanketrdeshmukh
closed
4 years ago
3
Fix long_description read in setup.py
#5
sroecker
closed
4 years ago
0
Implement non-constant learning rates
#4
lorenzwalthert
closed
4 years ago
1
TypeError: __init__() got an unexpected keyword argument 'min_weight_leaf'
#3
fganss
closed
5 years ago
2
mae criterion is very slow compared to mse or friedman_mse for classification
#2
flamby
closed
4 years ago
1
Method - update_terminal_regions in LossFunction Class- If Condition
#1
avinashbarnwal
closed
5 years ago
4