Question: # ! / usr / bin / env python # coding: utf - 8 # In [ 2 ] : from sklearn.neighbors import KNeighborsClassifier from
#usrbinenv python
# coding: utf
# In:
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracyscore, precisionscore, recallscore, confusionmatrix
def evaluateknnclassifierXtrain, ytrain, Xtest, ytest, bestk:
# Create and fit the KNN classifier with the bestk
knnclassifier KNeighborsClassifiernneighborsbestk
knnclassifier.fitXtrain, ytrain
# Make predictions on the test set
ypred knnclassifier.predictXtest
# Calculate evaluation metrics
accuracy accuracyscoreytest, ypred
precision precisionscoreytest, ypred
recall recallscoreytest, ypred
# Confusion matrix to get true positives and true negatives
tn fp fn tp confusionmatrixytest, ypredravel
return accuracy, precision, recall, tp tn
# Example usage:
# Assuming you have Xtrain, ytrain, Xtest, ytest, and bestk available
# accuracy, precision, recall, tp tn evaluateknnclassifierXtrain, ytrain, Xtest, ytest, bestk
# In:
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracyscore
def findbestkXtrain, ytrain, Xtest, ytest, kmax:
bestk
besterrorrate floatinf
for k in range kmax :
# Create and fit the KNN classifier with current k
knnclassifier KNeighborsClassifiernneighborsk
knnclassifier.fitXtrain, ytrain
# Make predictions on the test set
ypred knnclassifier.predictXtest
# Calculate error rate
errorrate accuracyscoreytest, ypred
# Update bestk and besterrorrate if a lower error rate is found
if errorrate besterrorrate:
bestk k
besterrorrate errorrate
return bestk besterrorrate
# Example usage:
# Assuming you have Xtrain, ytrain, Xtest, ytest, and kmax available
# bestk besterrorrate findbestkXtrain, ytrain, Xtest, ytest, kmax
# printfBest K: bestk with error rate: besterrorrate
# In:
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracyscore, precisionscore, recallscore, confusionmatrix
def evaluateknnclassifierXtrain, ytrain, Xtest, ytest, bestk:
# Create and fit the KNN classifier with the bestk
knnclassifier KNeighborsClassifiernneighborsbestk
knnclassifier.fitXtrain, ytrain
# Make predictions on the test set
ypred knnclassifier.predictXtest
# Calculate evaluation metrics
accuracy accuracyscoreytest, ypred
precision precisionscoreytest, ypred
recall recallscoreytest, ypred
# Confusion matrix to get true positives and true negatives
tn fp fn tp confusionmatrixytest, ypredravel
return accuracy, precision, recall, tp tn
# Example usage:
# Assuming you have Xtrain, ytrain, Xtest, ytest, and bestk available
# accuracy, precision, recall, tp tn evaluateknnclassifierXtrain, ytrain, Xtest, ytest, bestk
# printfAccuracy: accuracy Precision: precision Recall: recall True Positives: tp True Negatives: tn
# In:
from sklearn.svm import SVC
from sklearn.metrics import accuracyscore, precisionscore, recallscore, confusionmatrix
def evaluatesvmclassifierXtrain, ytrain, Xtest, ytest:
# Create and fit the SVM classifier with a linear kernel
svmclassifier SVCkernel'linear'
svmclassifier.fitXtrain, ytrain
# Make predictions on the test set
ypred svmclassifier.predictXtest
# Calculate evaluation metrics
accuracy accuracyscoreytest, ypred
precision precisionscoreytest, ypred
recall recallscoreytest, ypred
# Confusion matrix to get false positives and false negatives
tn fp fn tp confusionmatrixytest, ypredravel
# Get the coefficients of the decision function to find the slope and intercept
coef svmclassifier.coef
intercept svmclassifier.intercept
return coef, intercept, accuracy, precision, recall, fp fn
# Example usage:
# Assuming you have Xtrain, ytrain, Xtest, ytest available
# coef, intercept, accuracy, precision, recall, fp fn evaluatesvmclassifierXtrain, ytrain, Xtest, ytest
# printfSlope: coef Intercept: intercept
# printfAccuracy: accuracy Precision: precision Recall: recall
# printfFalse Positives: fp False Negatives: fn
the problem with my code is in the photo please find the erorr and provide the updated version. thank you
![#!/usr/bin/env python # coding: utf-8 # In[2]: from sklearn.neighbors import KNeighborsClassifier](https://dsd5zvtm8ll6.cloudfront.net/si.experts.images/questions/2024/09/66efb3594694c_04866efb358bdcab.jpg)
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