I have learned several classifiers in Machine learning - Decision tree, Neural network, SVM, Bayesian classifier, K-NN, Markov process...etc.
Can anyone please help to understand when I should prefer one of the classifier over other - for example - in which situation(nature of data sets, etc) I should prefer decision tree over neural net OR which situation SVM might work better than Bayesian OR what type s of problems are appropriate to apply decision tree. neural net or SVM or bayesian ??
Thanks.
The question you might want to ask is whether a linear model (Logistic Regression or Lasso) will explain the data better than a non-linear model (Random Forest). You might also want to consider whether you would like to have a generative model with a distribution over labels (Bayesian models) or a discriminative model (SVM). It's important to consider the number of features or degrees of freedom (Neural Networks contain millions of parameters vs. non-parametric model like K-NN).
In practice, it's common to choose a set of models based on the above criteria and ensemble them together, e.g. in a soft-voting classifier that takes into account predictions of several classifiers.