[FIXED] Create loop for 12 randomforests in Python scikit learn

Issue

I have twelve different datasets which aim at creating twelve different random forest models. I want to create a for-loop but I don't know how to store each model and name each model according to the correlated datasets (codes are depicted below). I am in the learning phase of learning python and machine learning using Scikit learn. Any comments or suggestions are welcome.

data = [df_AFC,df_AF,df_ESF,df_EXF,df_F,df_GF,df_KRFC,df_KRF,df_MF,df_PF,df_SFC,df_SF]
name = ['AFC','AF','ESF','EXF','F','GF','KRFC','KRF','MF','PF','SFC','SF']

#fix_random_state=42
result = []
for i,j in zip(data,name):
    x = i.drop('class', axis=1)
    y = i['class']
    
    # rus = RandomUnderSampler(sampling_strategy="not minority") # String
    rus = RandomUnderSampler(sampling_strategy=1, random_state=41) # Numerical value
    x_res, y_res = rus.fit_resample(x, y)
    
    #Remove low variance features
    #replace with x_res, y_res from now on
    remove_low_variance(x_res, threshold=0.1)
    
    #Data splitting
    x_train, x_test, y_train, y_test = train_test_split(x_res, y_res, test_size=0.2, random_state=42)
    x_train.shape, x_test.shape
    
    #Model building
    model = RandomForestClassifier(n_estimators=500, random_state=42)
    model.fit(x_train, y_train)
    result.append(model_i)

expected outputs

y_train_pred_AFC = model_AFC.predict(unknown)
y_train_pred_AF = model_AF.predict(unknown)
...

Solution

I'd recommend storing the models in a dictionary like this:

data_sets = [df_AFC,df_AF,df_ESF,df_EXF,df_F,df_GF,df_KRFC,df_KRF,df_MF,df_PF,df_SFC,df_SF]
names = ['AFC','AF','ESF','EXF','F','GF','KRFC','KRF','MF','PF','SFC','SF']

#fix_random_state=42
models = {}
for data, name in zip(data_sets, names):

    # <<< REMOVED CODE >>>
    
    #Model building
    model = RandomForestClassifier(n_estimators=500, random_state=42)
    model.fit(x_train, y_train)
    models[name] = model

Then you can call the trained model to make predictions in another loop like this:

predictions = {}
for name, model in models.items():
    predictions[name] = model.predict(test_data)

Or one by one:

y_train_pred_AFC = models['AFC'].predict(test_data)
#...

Answered By - TC Arlen