\'\'\' Following is an example function of k-fold cross validation. - In this co

Question

 ''' Following is an example function of k-fold cross validation.   - In this code, you are given the dataset, x_train and y_train. And you try to find the      best model with k-fold cross validation. - There are 10 blanks (underlined) for you to fill out. - The goal is not to write out the perfect code, but to understand how k-fold cross      validation is actually implemented by studying an example. - Please also note that this is mostly pseudo-code although much of this code is probably     runnable code :) '''  def k_fold_cross_validation(x_train, y_train) # x_train and y_train contains all the training data # initial best_model and best_score     best_model          # variable unassigned at present     best_score = 0      # variable initialized to 0     for each model:         num_folds = 3  # the number of folds         subset_size = len(x_train) / _________  # subset_size = size of each fold of data                  test_model = sklearn.svm.SVC() or other models                   # SVC stands for Support Vector Classification. It is a built-in class in                  # sklearn. We can initialize the model we want to test in this loop         correct = 0 # keeps the number of correct predictions              for i in range(________):             # In k-fold cross validation, we split the dataset into k folds. We use one                          # fold of data to test the model, and use the rest of the data to train the                          # model. The following 4 lines of code shows how we extract particular fold                         # of data from the whole data set.   For example, if we want to use the i-th                         # fold of data to test, given the subset_size, the index of i-th fold data                          # is from i * subset_size to (i + 1) * subset_size                          training_this_round_xVal = x_train[:i * subset_size] + x_train[(i + 1) * subset_size:]             training_this_round_yVal = y_train[:i * subset_size] + y_train[(i + 1) * subset_size:]             testing_this_round_xVal = x_train[________:][:subset_size]             testing_this_round_yVal = y_train[________:][:subset_size]                          # use training data to train the model             test_model.fit(________,________)                          # predict using trained model             y_predict = test_model.predict(________)                          # sum up the correct predicted data             for i in range(0, len(y_predict)):                 if (y_predict[i] == ________):  # prediction == actual?                     correct += 1                              # save the best score and best model so far         if (float(correct)  / len(y_train) > best_score): # If current score better...             best_score = float(________)  / len(y_train)  # ... save the better score             best_model = ________ # ... and save current model that resulted in best score              return best_score, best_model 

Explanation / Answer

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def k_fold_cross_validation(x_train, y_train):
# x_train and y_train contains all the training data
# initial best_model and best_score
best_model # variable unassigned at present
best_score = 0 # variable initialized to 0
for each model:
num_folds = 3 # the number of folds
subset_size = len(x_train) / num_folds # subset_size = size of each fold of data
  
test_model = sklearn.svm.SVC() or other models
# SVC stands for Support Vector Classification. It is a built-in class in
# sklearn. We can initialize the model we want to test in this loop
correct = 0 # keeps the number of correct predictions
  
for i in range(num_folds):
# In k-fold cross validation, we split the dataset into k folds. We use one
# fold of data to test the model, and use the rest of the data to train the
# model. The following 4 lines of code shows how we extract particular fold
# of data from the whole data set. For example, if we want to use the i-th
# fold of data to test, given the subset_size, the index of i-th fold data
# is from i * subset_size to (i + 1) * subset_size
  
training_this_round_xVal = x_train[:i * subset_size] + x_train[(i + 1) * subset_size:]
training_this_round_yVal = y_train[:i * subset_size] + y_train[(i + 1) * subset_size:]
testing_this_round_xVal = x_train[i:][:subset_size]
testing_this_round_yVal = y_train[i:][:subset_size]
  
# use training data to train the model
test_model.fit(training_this_round_xVal,training_this_round_yVal)
  
# predict using trained model
y_predict = test_model.predict(testing_this_round_xVal)
  
# sum up the correct predicted data
for i in range(0, len(y_predict)):
if (y_predict[i] == training_this_round_yVal[i]): # prediction == actual?
correct += 1
  
# save the best score and best model so far
if (float(correct) / len(y_train) > best_score): # If current score better...
best_score = float(correct) / len(y_train) # ... save the better score
best_model = test_model # ... and save current model that resulted in best score
  
return best_score, best_model

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