I'm a newbie in machine learning field. And I need to choose the best model for my classification model, so i use learning curve from sk-learn to make selection. I train and plot learning curve on models such:Logistic RegressionKNNGaussian Naive BayesDecision TreeRandom forestStochastic Gradient DescentPerceptronSVMLinear SVCHere is detail graphs:So I don't know how to conclude from above pictures:How bias vs variance on each model (underestimate/over estimate) ?Which models need more data to train or need to increase the complex ( increase degree,...)Which models is good to apply?Which modes is the best to select?Please help me clarify these answers relate to these learning curves.Thanks a lot
Learning curve for model selection
machine learning;learning theory