semvec/pypi-packages huggingface dataset:
weightedmodels-glazed 1.0
A library to help you ensemble models
Requires Python: >=3.6

# WeightedModels
A python library allowing you to use multiple models using the weight of each model based on their performance

Example code:

```
model1 = catboost.CatBoostRegressor()
model2 = RandomForestRegressor()
model3 = xgboost.XGBRegressor()

my_data = pd.read('my_data.csv')
test = pd.read('test.csv)

my_models = [model1, model2, model3]
models = WeightedModels( models = my_models, trainSplit = 0.8, randomState = 696969 )

models.fit(my_data)

print(models.getModelWeights())

myPredictedData = models.predict(test)
```
weightedmodels-glazeddonut 1.0
A library to help you ensemble models
Requires Python: >=3.6

# WeightedModels
A python library allowing you to use multiple models using the weight of each model based on their performance

Example code:

```
model1 = catboost.CatBoostRegressor()
model2 = RandomForestRegressor()
model3 = xgboost.XGBRegressor()

my_data = pd.read('my_data.csv')
test = pd.read('test.csv)

my_models = [model1, model2, model3]
models = WeightedModels( models = my_models, trainSplit = 0.8, randomState = 696969 )

models.fit(my_data)

print(models.getModelWeights())

myPredictedData = models.predict(test)
```