VotingRegressor(estimators=[('ET', ExtraTreesRegressor()),
('XGB',
XGBRegressor(alpha=0.5, base_score=0.5,
booster='gbtree', colsample_bylevel=1,
colsample_bynode=1,
colsample_bytree=0.4,
enable_categorical=False, gamma=0,
gpu_id=-1, importance_type=None,
interaction_constraints='',
learning_rate=0.2, max_delta_step=0,
max_depth=2, min_child_weight=1,
missing=nan,
mo...
n_estimators=1000, n_jobs=8,
num_parallel_tree=1, predictor='auto',
random_state=0, reg_alpha=0.5,
reg_lambda=1, scale_pos_weight=1,
subsample=1, tree_method='exact',
validate_parameters=1,
verbosity=None)),
('RF', RandomForestRegressor(max_depth=100)),
('AB',
AdaBoostRegressor(base_estimator=RandomForestRegressor(max_depth=50,
n_estimators=50),
learning_rate=0.001))])
**How to use the trained model for inference**
```python
import json
from joblib
import pandas as pd
Tc_predictor = load('Magnet_Tc_predictor.joblib') # trained model
config = json.load(open('config.json')) # config file
features = config['features'] # feature extraction
#data = pd.read_excel("data.xlsx") # read test file with new compositions
data = data[features]
#data.columns = ["feat_" + str(col) for col in data.columns]
Predicted_value = Tc_predictor.predict(data) # predict Tc values
print("Predicted Tc value is: {0:.2f}'.format(predictions)")
```