Combine results from multiple models I am using chunks of 100000 rows at a time from the CSV file to train the a simple LASSO model. 
  
  How do i combine all of these models trained from these different chunks? I would like to use all these trained models for prediction
  
  I am familiar with DASK and other alternatives but i would like to use Pandas. 


pipelines = {
    'lasso' : make_pipeline(StandardScaler(), Lasso(random_state=123))
}

for key, value in pipelines.items():
    print( key, type(value) )

# Lasso hyperparameters
lasso_hyperparameters = { 
    'lasso__alpha' : [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10] 
}

hyperparameters = {
'lasso' : lasso_hyperparameters
}

# Create empty dictionary called fitted_models
fitted_models = {}

# Create cross-validation object from pipeline and hyperparameters
model = GridSearchCV(pipeline, hyperparameters[name], cv=10, n_jobs=-1)

def train(X_train, y_train):  
    # Fit model on X_train, y_train
    model.fit(X_train, y_train)

    # Store model in fitted_models[name] 
    fitted_models[name] = model

    # Print '{name} has been fitted'
    print(name, 'has been fitted.')
    print ("__________________________________")
    print (model.cv_results_)


for df in pd.read_csv('train_V2.csv', chunksize=100000):
    df = df.dropna()
    df = pd.get_dummies(df, columns=['matchType'])
    df_train = df.drop(['Id', 'groupId', 'matchId'], axis = 1)
    y = df_train.winPlacePerc       
    X = df_train.drop('winPlacePerc', axis=1)
    X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                                    test_size=0.2, 
                                                    random_state=1234)
    X_train = np.asarray(X_train)
    X_test = np.asarray(X_test)
    y_train = np.asarray(y_train)
    y_test = np.asarray(y_test)

    train(X_train, y_train)
What you are looking for is called "stochastic optimization". You don't need to fit separate models and then combine them.