regression_model_dict.py

from sklearn.tree               import DecisionTreeRegressor
from sklearn.ensemble           import ExtraTreesRegressor, AdaBoostRegressor, RandomForestRegressor, GradientBoostingRegressor
from sklearn.neural_network     import MLPRegressor
from sklearn.svm                import SVR
from sklearn.linear_model       import Ridge, Lasso, SGDRegressor, BayesianRidge
from sklearn.neighbors          import KNeighborsRegressor, RadiusNeighborsRegressor
from sklearn.experimental       import enable_hist_gradient_boosting
from sklearn.ensemble           import HistGradientBoostingRegressor
from catboost                   import CatBoostRegressor
from lightgbm                   import LGBMRegressor
tree_regressors = {
    "Decision_tree_regressor": DecisionTreeRegressor(),
    "AdaBoost_regressor": AdaBoostRegressor(),
    "Extra_trees_regressor": ExtraTreesRegressor(),
    "Random_forest_regressor": RandomForestRegressor(), # Takes 55 seconds
    "GBM_regressor": GradientBoostingRegressor(), #Takes forever
    "HGB_regressor": HistGradientBoostingRegressor(),
    "CATBoost_regressor": CatBoostRegressor(verbose=0),
    "lightgbm_regressor": LGBMRegressor(),
        }
mult_regeressors = {
    "Linear_regression": LinearRegression(), ### Dont use results were awful
    "Ridge_regressor": Ridge(),
    "SVM_regressor": SVR(), # Takes 150  seconds
    "MLP_regressor": MLPRegressor(),
    "SGD_regressor": SGDRegressor(),
    "KNN_regressor": KNeighborsRegressor(),
    "BR_regressor" : BayesianRidge(),
    "RNN_regressor": RadiusNeighborsRegressor(), # Predicts NaN's :S
        }