Comparing 10 different ML models to find the best one for classification of breast cancer

Predicting the possibility of having breast cancer using different ML models

Logistic Regression

Confusion Matrix:

[[ 62 1]

[ 2 106]]

Classification Report:

heading	precision	recall	f1-score	support
0	0.97	0.98	0.98	63
1	0.99	0.98	0.99	108
accuracy			0.98	171
macro avg	0.98	0.98	0.98	171
weighted avg	0.98	0.98	0.98	171

AUC Score: 0.9980893592004703

KNN

Confusion Matrix:

[[ 59 4]

[ 3 105]]

Classification Report:

heading	precision	recall	f1-score	support
0	0.95	0.94	0.94	63
1	0.96	0.97	0.97	108
accuracy			0.96	171
macro avg	0.96	0.95	0.96	171
weighted avg	0.96	0.96	0.96	171

SVM

Confusion Matrix:

[[ 61 2]

[ 3 105]]

Classification Report:

heading	precision	recall	f1-score	support
0	0.95	0.97	0.96	63
1	0.98	0.97	0.98	108
accuracy			0.97	171
macro avg	0.97	0.97	0.97	171
weighted avg	0.97	0.97	0.97	171

AUC Score: 0.9964726631393297

Decision Tree

Confusion Matrix:

[[59 4]

[ 9 99]]

Classification Report:

heading	precision	recall	f1-score	support
0	0.87	0.94	0.90	63
1	0.96	0.92	0.94	108
accuracy			0.92	171
macro avg	0.91	0.93	0.92	171
weighted avg	0.93	0.92	0.92	171

AUC Score: 0.9265873015873015

Random Classifier

Confusion Matrix:

[[ 59 4]

[ 2 106]]

Classification Report:

heading	precision	recall	f1-score	support
0	0.97	0.94	0.95	63
1	0.96	0.98	0.97	108
accuracy			0.96	171
macro avg	0.97	0.96	0.96	171
weighted avg	0.96	0.96	0.96	171

AUC Score: 0.9961787184009406

Gradient Boosting

Confusion Matrix:

[[ 59 4]

[ 3 105]]

Classification Report:

heading	precision	recall	f1-score	support
0	0.95	0.94	0.94	63
1	0.96	0.97	0.97	108
accuracy			0.96	171
macro avg	0.96	0.95	0.96	171
weighted avg	0.96	0.96	0.96	171

AUC Score: 0.9954438565549677

Naive Bayes

Confusion Matrix:

[[ 57 6]

[ 5 103]]

Classification Report:

heading	precision	recall	f1-score	support
0	0.92	0.90	0.91	63
1	0.94	0.95	0.95	108
accuracy			0.94	171
macro avg	0.93	0.93	0.93	171
weighted avg	0.94	0.94	0.94	171

AUC Score: 0.9926513815402704

Neural Networks

Confusion Matrix:

[[ 61 2]

[ 2 106]]

Classification Report:

heading	precision	recall	f1-score	support
0	0.97	0.97	0.97	63
1	0.98	0.98	0.98	108
accuracy			0.98	171
macro avg	0.97	0.97	0.97	171
weighted avg	0.98	0.98	0.98	171

AUC Score: 0.9966196355085244

AdaBoost

Confusion Matrix:

[[ 61 2]

[ 2 106]]

Classification Report:

heading	precision	recall	f1-score	support
0	0.97	0.97	0.97	63
1	0.98	0.98	0.98	108
accuracy			0.98	171
macro avg	0.97	0.97	0.97	171
weighted avg	0.98	0.98	0.98	171

AUC Score: 0.9961787184009406

XGBoost

Confusion Matrix:

[[ 61 2]

[ 3 105]]

Classification Report:

heading	precision	recall	f1-score	support
0	0.95	0.97	0.96	63
1	0.98	0.97	0.98	108
accuracy			0.97	171
macro avg	0.97	0.97	0.97	171
weighted avg	0.97	0.97	0.97	171

AUC Score: 0.9944150499706055