code: from sklearn import tree
  from sklearn import neighbors
  from sklearn import gaussian_process
  
  #[height, weight, shoe size]
  X = [[181,80,10],[161,70,6],[171,66,7],[176,88,7],[189,100,8],[141,80,5],[156,78,6],[161,50,6],[171,60,7],[151,78,7],[171,40,7]]
  #Gender
  Y = ['male','male','male','male','male','female','female','female','female','female','female']
  
  #Define 'DecisionTreeClassifier' Classifier From the imported Tree
  decisionTreeclassifier = tree.DecisionTreeClassifier()
  
  #Fit the data into the Classifier
  decisionTreeclassifier = decisionTreeclassifier.fit(X,Y)
  
  #Perform Prediction
  decisionTreeclassifierPrediction = decisionTreeclassifier.predict([[161,60,9]])
  
  #Print the Classifier
  print(decisionTreeclassifier)
  
  #Print the Prediction
  print(decisionTreeclassifierPrediction)
  
  
  
  #Define 'KNeighborsClassifier' Classifier From the imported Tree
  kNeighborsClassifier = neighbors.KNeighborsClassifier()
  
  #Fit the data into the Classifier
  kNeighborsClassifier = kNeighborsClassifier.fit(X,Y)
  
  #Perform Prediction
  kNeighborsClassifierPrediction = kNeighborsClassifier.predict([[161,60,9]])
  
  #Print the Classifier
  print(kNeighborsClassifier)
  
  #Print the Prediction
  print(kNeighborsClassifierPrediction)
  
  
  
  #Define 'GaussianProcessClassifier' Classifier From the imported Tree
  gaussianProcessClassifier = gaussian_process.GaussianProcessClassifier()
  
  #Fit the data into the Classifier
  gaussianProcessClassifier = gaussianProcessClassifier.fit(X,Y)
  
  #Perform Prediction
  gaussianProcessClassifierPrediction = gaussianProcessClassifier.predict([[161,60,9]])
  
  #Print the Classifier
  print(gaussianProcessClassifier)
  
  #Print the Prediction
  print(gaussianProcessClassifierPrediction)
apis: sklearn.tree.DecisionTreeClassifier sklearn.neighbors.KNeighborsClassifier sklearn.gaussian_process.GaussianProcessClassifier