(Solved) : Question Python Codes Adaboost Classifier Using Weak Linear Classifiers Run Codes Fix Pro Q42693004 . . .

Question: Below are python codes about “Adaboost Classifierusing weak linear classifiers”. Run the codes and fix the problemof the error.

class Boosting:
def __init__(self,dataset,T,test_dataset):
self.dataset = dataset
self.T = T
self.test_dataset = test_dataset
self.alphas = None
self.models = None
self.accuracy = []
self.predictions = None
def fit(self):
# Set the descriptive features and the target feature
X = self.dataset.drop([‘target’],axis=1)
Y =self.dataset[‘target’].where(self.dataset[‘target’]==1,-1)
# Initialize the weights of each sample with wi = 1/N and create adataframe in which the evaluation is computed
Evaluation = pd.DataFrame(Y.copy())
Evaluation[‘weights’] = 1/len(self.dataset) # Set the initialweights w = 1/N
# Run the boosting algorithm by creating T “weighted models”
alphas = []
models = []
for t in range(self.T):
# Train the Decision Stump(s)
Tree_model =DecisionTreeClassifier(criterion=”entropy”,max_depth=1) # Mind thedeth one –> Decision Stump
# We know that we must train our decision stumps on weighteddatasets where the weights depend on the results of
# the previous decision stumps. To accomplish that, we use the’weights’ column of the above created
# ‘evaluation dataframe’ together with the sample_weight parameterof the fit method.
# The documentation for the sample_weights parameter sais: “[…]If None, then samples are equally weighted.”
# Consequently, if NOT None, then the samples are NOT equallyweighted and therewith we create a WEIGHTED dataset
# which is exactly what we want to have.
model =Tree_model.fit(X,Y,sample_weight=np.array(Evaluation[‘weights’]))
# Append the single weak classifiers to a list which is later onused to make the
# weighted decision
predictions = model.predict(X)
score = model.score(X,Y)
# Add values to the Evaluation DataFrame
Evaluation[‘predictions’] = predictions
Evaluation[‘evaluation’] = np.where(Evaluation[‘predictions’] ==Evaluation[‘target’],1,0)
Evaluation[‘misclassified’] = np.where(Evaluation[‘predictions’] !=Evaluation[‘target’],1,0)
# Calculate the misclassification rate and accuracy
accuracy =sum(Evaluation[‘evaluation’])/len(Evaluation[‘evaluation’])
misclassification =sum(Evaluation[‘misclassified’])/len(Evaluation[‘misclassified’])
# Caclulate the error
err =np.sum(Evaluation[‘weights’]*Evaluation[‘misclassified’])/np.sum(Evaluation[‘weights’])

# Calculate the alpha values
alpha = np.log((1-err)/err)
# Update the weights wi –> These updated weights are used inthe sample_weight parameter
# for the training of the next decision stump.
Evaluation[‘weights’] *=np.exp(alpha*Evaluation[‘misclassified’])
#print(‘The Accuracy of the {0}. model is :’.format(t+1),accuracy*100,’%’)
#print(‘The missclassification rate is:’,misclassification*100,’%’)
self.alphas = alphas
self.models = models
def predict(self):
X_test =self.test_dataset.drop([‘target’],axis=1).reindex(range(len(self.test_dataset)))
Y_test =self.test_dataset[‘target’].reindex(range(len(self.test_dataset))).where(self.dataset[‘target’]==1,-1)
# With each model in the self.model list, make a prediction
accuracy = []
predictions = []
for alpha,model in zip(self.alphas,self.models):
prediction = alpha*model.predict(X_test) # We use the predictmethod for the single decisiontreeclassifier models in thelist
self.predictions =np.sign(np.sum(np.array(predictions),axis=0))

######Plot the accuracy of the model against the number ofstump-models used##########
number_of_base_learners = 50
fig = plt.figure(figsize=(10,10))
ax0 = fig.add_subplot(111)
for i in range(number_of_base_learners):
model = Boosting(dataset,i,dataset)
ax0.set_xlabel(‘# models used for Boosting ‘)
print(‘With a number of ‘,number_of_base_learners,’base models wereceive an accuracy of ‘,model.accuracy[-1]*100,’%’)


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Answer to Question: Below are python codes about “Adaboost Classifier using weak linear classifiers”. Run the codes and fix the pr…

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