ML | Vote classifier using Sklearn

Michael Zippo

The voting classifier supports two types of voting.

Hard voting. In hard voting, the predicted output class is — it is the class with the highest majority of votes, that is, the class that has the highest probability of being predicted by each of the classifiers. Suppose three classifiers predicted output class (A, A, B) , so here most people predicted A as output. Therefore, A will be the final prediction.
Soft vote: With soft vote, the output class is — it is a prediction based on the average probability given to this class. Suppose given some input for three models: the prediction probability for the class A = (0.30, 0.47, 0.53) and B = (0.20, 0, 32, 0.40) . Thus, the average for class A is 0.4333, and B — 0.3067 , the winner is clearly A, as it had the highest probability averaged over each classifier.

Note. Make sure you include the different models for the submission of the qualifier to vote to ensure that a mistake made by one can be corrected by the other.
Code: Python code to implement the voting classifier

# import libraries

from sklearn.ensemble import VotingClassifier

from sklearn.linear_model import LogisticRegression

from sklearn.svm import SVC

from sklearn.tree import DecisionTreeClassifier

from sklearn.datasets impor t load_iris

from sklearn.metrics import accuracy_score

from sklearn.model_selection import train_test_split

# load the iris dataset

iris = load_iris ()

X = iris.data [:,: 4 ]

Y = iris. target

# train_test_split

X_train, X_test, y_train, y_test = train_test_split (X,

Y,

test_size = 0.20 ,

random_state = 42 )

# model group / ensemble

estimator = []

estimator.append (( ’ LR’ ,

LogisticRegression (solver = ’lbfgs’ ,

     multi_class   =   ’multinomial’  , 
   max_iter   =   200  ))) 
   estimator.append ((  ’SVC’  , SVC (gamma   =   ’auto’  , probability   =   True  ))) 
   estimator.append ((  ’DTC ’ , DecisionTreeClassifier ())) 
    
  # Vote classifier with gesture by vote  
   vot_hard   =   VotingClassifier (estimators   =   estimator, voting   =  ’ hard’  ) 
   vot_hard.fit (X_train, y_train)  
   y_pred   =   vot_hard.predict (X_test)  
   
  # using precision_score metric to predict accuracy  
   score   =   accuracy_score (y_test, y_pred)  
   print   (  "Hard Voting Score% d"  %   score) 
    
  # Soft vote classifier  
   vot_soft   =   VotingClassifier (estimators   =   estimator , voting   =   ’soft’  )  
   vot_soft.fit (X_train, y_train) 
   y_pred   =   vot_soft.predict (X_test) 
    
  # using precision_score  
   score   =   accuracy_score (y_test, y_pred) 
   print   (  "Soft Voting Score% d"  %   score)

Exit :

 Hard Voting Score 1 Soft Voting Score 1

Examples :

  Input: 4.7, 3.2, 1.3, 0.2   Output: Iris Setosa

In practice, the output accuracy will be higher for a soft vote, since this is the average probability of all evaluators combined, since for we are already retraining our basic iris dataset, so there won’t be much difference in the output.

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ML | Vote classifier using Sklearn

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