Q: Look for the multiclass classifiers in Logistic Regression, k$-$Nearest Neighbors and Support Vector

Machine. Apply them to analyze AutoMPG and discuss the results. The target is to classify the $$origin$$ of the

car and $$mpg$$ can be included in the X$.$

Code:

import pandas as pd

import numpy as np

from sklearn.linear$\_$model import LogisticRegression

from sklearn.neighbors import KNeighborsClassifier

from sklearn.svm import SVC

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.metrics import accuracy$\_$score

# Load the dataset

auto $=$ pd$.$read$\_$csv$($r$"$C:$\backslash \backslash$Users$\backslash .$csv$")$

# Create X and y

X $=$ auto$[[\text{'}$mpg$\text{'},$ 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model year'$]]$

y $=$ auto$[\text{'}$origin$\text{'}]$

# Split the data into training and testing sets

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}3,$ random$\_$state$=42)$

# Create and fit the model

lr $=$ LogisticRegression$($multi$\_$class$=$'multinomial', solver$=$'newton$-$cg$\text{'})$

lr$.$fit$($X$\_$train, y$\_$train$)$

# Make predictions on the test set

lr$\_$pred $=$ lr$.$predict$($X$\_$test$)$

# Evaluate the model

lr$\_$accuracy $=$ accuracy$\_$score$($y$\_$test, lr$\_$pred$)$

print$(\text{'}$Accuracy of LR model:$\text{'},100*$lr$\_$accuracy, $\text{'}\%\text{'})$

# Create and fit the model

knn $=$ KNeighborsClassifier$($n$\_$neighbors$=5)$

knn$.$fit$($X$\_$train, y$\_$train$)$

# Make predictions on the test set

knn$\_$pred $=$ knn$.$predict$($X$\_$test$)$

# Evaluate the model

knn$\_$accuracy $=$ accuracy$\_$score$($y$\_$test, knn$\_$pred$)$

print$(\text{'}$Accuracy of KNN model:$\text{'},$ knn$\_$accuracy$)$

# Create and fit the model

svc $=$ SVC$($kernel$=$'linear', C$=1,$ decision$\_$function$\_$shape$=$'ovr'$)$

svc$.$fit$($X$\_$train, y$\_$train$)$

# Make predictions on the test set

svc$\_$pred $=$ svc$.$predict$($X$\_$test$)$

# Evaluate the model

svc$\_$accuracy $=$ accuracy$\_$score$($y$\_$test, svc$\_$pred$)$

print$(\text{'}$Accuracy of SVM model: $\text{'},$ svc$\_$accuracy$)$

As for the question, I got the above code to calculate the "accuracy". Can you give me the code to calculate "sensitivity", "specificity", and "precision"? And improve the above code. Thank you!