> Is it typical that ~2000 samples (1000 leads, 1000 non-leads) is "enough"? In terms of practical machine learning, plotting learning curves are a great way t

> Is it typical that ~2000 samples (1000 leads, 1000 non-leads) is "enough"? In terms of practical machine learning, plotting learning curves are a great way to know if samples are "enough" [0, 1]. If your algorithm under fits (high bias) both errors (training and validation) will be high and in this case, adding training samples would not help. [0] http://www.astroml.org/sklearn_tutorial/practical.html http://www.astroml.org/sklearn_tutorial/practical.html [1] http://blog.fliptop.com/blog/2015/03/02/bias-variance-and-overfitting-machine-learning-overview/ http://blog.fliptop.com/blog/2015/03/02/bias-variance-and-ov...