I have a dataset consisting of 181 samples(classes are not balanced there are 41 data points with 1 label and rest 140 are with label 0) and 10 features and one target variable. The 10 features are numeric and continuous in nature. I have to perform binary classification. I have done the following work:-
I have performed 3 Fold cross validation and got following accuracy results using various models:-
LinearSVC:
0.873
DecisionTreeClassifier:
0.840
Gaussian Naive Bayes:
0.845
Logistic Regression:
0.867
Gradient Boosting Classifier
0.867
Support vector classifier rbf:
0.818
Random forest:
0.867
K-nearest-neighbors:
0.823
Please guide me how could I choose the best model for this size of dataset and make sure my model is not overfitting ? I am thinking of applying random under sampling to handle the unbalanced data.