Decision boundaries learnt by training with some R library classifiers

1. A 2-D non-separable dataset (generated uniformly and randomly) is used to train and generate the decision boundaries with a few of the following R library classifiers.
2. The following are the decision boundaries learnt by different ML models from a few R packages, when trained on the above dataset.
3. The following are the decision boundaries learnt a few linear classifiers such as (logistic regression, with / with L1/L2 penalties, linear discriminant analysis) and also with Naive Bayes.
   
4. The following are the decision boundaries learnt by k-nearest neighbor algorithm with different values of k.
   
5. The following are the decision boundaries learnt by a backpropagation neural net with single hidden layer with different numbers of hidden units.
   
6. The following are the decision boundaries learnt by the CART decision tree with different values of the pruning parameter.
   
7. The following are the decision boundaries learnt by the random forest ensemble classifier with different numbers of trees.
   
8. The following are the decision boundaries learnt by the support vector machine classifier with different values of the C parameter.
   
9. As can be seen, the models overfit / underfit the training data with different values of the parameters used for learning.