How can I fix this "convex" problem ? Is it just a matter of overfitting? I get some metrics on validation data while training a model , and in my case the they are :


  (0.25, 0.31, 0.46, 0.57, 0.65, 0.75, 0.77, 0.78, 0.84, 0.84, 0.85,
  0.84, 0.84, 0.84, 0.82, 0.8, 0.8, 0.79, 0.78, 0.77, 0.77, 0.77, 0.75, 0.74, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73)


They can described like this :



In my view , the ideal result should be like :


Is it a matter of overfitting ? 

Unfortunately , I tried few times to change the regular coefficients to avoid overfitting , and adjust learning rate coefficients to slow down , but it was still "convex" . 

How can I achieve the ideal result showed above ? 

Much appreciated if anyone would give me some constructive tips ?
Yes, what you are seeing is a classic case of overfitting. 

You stated that you use a linear model such as logistic regression. To regularize these types of models, usually L1 and/or L2 regularization is applied. L1 regularization is simply $||W||_1$ and L2 is $||W||_2^2$ usually.

Another method is to alter the labels of the model in a specific way, which is a method of regularization I created (shameless plug). Here is the link to the paper: https://arxiv.org/abs/1609.06693

Hope this helps.