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 Regression
- Gaussian Naive Bayes
- Decision Tree
- Random forest
- Stochastic Gradient Descent
- Linear SVC
Here 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