code: def run_experiment(classifier="SVM", feature_set="hog", dir_names=[]):
      print("Loading dataset. This will take time ...")
      features, labels = load_dataset(feature_set, dir_names)
      print("Finished loading dataset.")
  
      classifiers, random_seed = load_classifiers()
  
      (
          train_features,
          test_features,
          train_labels,
          test_labels,
      ) = train_test_split(
          features, labels, test_size=0.2, random_state=random_seed
      )
  
      model = classifiers[classifier]
      print("############## Training", classifier, "##############")
      model.fit(train_features, train_labels)
      accuracy = model.score(test_features, test_labels)
      print(classifier, "accuracy:", accuracy * 100, "%")
  
      return model, accuracy
  
file_path: Mozart/src/train.py
imports: import cv2
  import random
  import imutils
  from glob import glob
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.neural_network import MLPClassifier
  from sklearn import svm
  import numpy
  import matplotlib.pyplot
  import pickle
label_desc: Use image node_id 7 for calling a global function with example usage: run_experiment(classifier, feature_set, dir_names) and returns: model,  accuracy