Machine Learning; Compare performance of model of both before and after hyperparameter tuning data stated below. Please analyze for below data only. Not the general

Before hyperparameter Tuning of KNN model:

Accuracy: $0\mathrm{.}5180,$ Precision: $0\mathrm{.}5319,$ Recall: $0\mathrm{.}4883,$ F$1-$Score: $0\mathrm{.}5092$

Confusion Matrix:

$[[134110]$

$[131125]]$

Complete Report of KNN Model:

precision recall f$1-$score support

$00\mathrm{.}510\mathrm{.}550\mathrm{.}53244$

$10\mathrm{.}530\mathrm{.}490\mathrm{.}51256$

accuracy $0\mathrm{.}52500$

macro avg $0\mathrm{.}520\mathrm{.}520\mathrm{.}52500$

weighted avg $0\mathrm{.}520\mathrm{.}520\mathrm{.}52500$

$---------------------------------------$

After hyperparameter Tuning of KNN model:

Accuracy: $0\mathrm{.}5280,$ Precision: $0\mathrm{.}5417,$ Recall: $0\mathrm{.}5078,$ F$1-$Score: $0\mathrm{.}5242$

Confusion Matrix:

$[[134110]$

$[126130]]$

Complete Report of KNN Model:

precision recall f$1-$score support

$00\mathrm{.}520\mathrm{.}550\mathrm{.}53244$

$10\mathrm{.}540\mathrm{.}510\mathrm{.}52256$

accuracy $0\mathrm{.}53500$

macro avg $0\mathrm{.}530\mathrm{.}530\mathrm{.}53500$

weighted avg $0\mathrm{.}530\mathrm{.}530\mathrm{.}53500$

$---------------------------------------$

Before hyperparameter Tuning of Naive Bayes model:

Accuracy: $0\mathrm{.}4640,$ Precision: $0\mathrm{.}4744,$ Recall: $0\mathrm{.}4336,$ F$1-$Score: $0\mathrm{.}4531$

Confusion Matrix:

$[[121123]$

$[145111]]$

Complete Report of Naive Bayes Model:

precision recall f$1-$score support

$00\mathrm{.}450\mathrm{.}500\mathrm{.}47244$

$10\mathrm{.}470\mathrm{.}430\mathrm{.}45256$

accuracy $0\mathrm{.}46500$

macro avg $0\mathrm{.}460\mathrm{.}460\mathrm{.}46500$

weighted avg $0\mathrm{.}460\mathrm{.}460\mathrm{.}46500$

$---------------------------------------$

After hyperparameter Tuning of Naive Bayes model:

Accuracy: $0\mathrm{.}4680,$ Precision: $0\mathrm{.}4728,$ Recall: $0\mathrm{.}3398,$ F$1-$Score: $0\mathrm{.}3955$

Confusion Matrix:

$[[14797]$

$[16987]]$

Complete Report of Naive Bayes Model:

precision recall f$1-$score support

$00\mathrm{.}470\mathrm{.}600\mathrm{.}53244$

$10\mathrm{.}470\mathrm{.}340\mathrm{.}40256$

accuracy $0\mathrm{.}47500$

macro avg $0\mathrm{.}470\mathrm{.}470\mathrm{.}46500$

weighted avg $0\mathrm{.}470\mathrm{.}470\mathrm{.}46500$

$---------------------------------------$

Before hyperparameter Tuning of Random Forest model:

Accuracy: $0\mathrm{.}4960,$ Precision: $0\mathrm{.}5085,$ Recall: $0\mathrm{.}4688,$ F$1-$Score: $0\mathrm{.}4878$

Confusion Matrix:

$[[128116]$

$[136120]]$

Complete Report of Random Forest Model:

precision recall f$1-$score support

$00\mathrm{.}480\mathrm{.}520\mathrm{.}50244$

$10\mathrm{.}510\mathrm{.}470\mathrm{.}49256$

accuracy $0\mathrm{.}50500$

macro avg $0\mathrm{.}500\mathrm{.}500\mathrm{.}50500$

weighted avg $0\mathrm{.}500\mathrm{.}500\mathrm{.}50500$

$---------------------------------------$

After hyperparameter Tuning of Random Forest model:

Accuracy: $0\mathrm{.}5220,$ Precision: $0\mathrm{.}5341,$ Recall: $0\mathrm{.}5195,$ F$1-$Score: $0\mathrm{.}5267$

Confusion Matrix:

$[[128116]$

$[123133]]$

Complete Report of Random Forest Model:

precision recall f$1-$score support

$00\mathrm{.}510\mathrm{.}520\mathrm{.}52244$

$10\mathrm{.}530\mathrm{.}520\mathrm{.}5325$