It’s hard to find good off-the-shelf tools for practical machine learning. Many of the projects are aimed at students and researchers who want access to the inner workings of the algorithms, which can be off-putting when you’re looking for more of a black box to solve a particular problem. That’s a gap that scikits.learn really helps to fill. It’s a beautifully documented and easy-to-use Python package offering a high-level interface to many standard machine learning techniques.
It collects most techniques that fall under the standard definition of machine learning (taking a training dataset and using that to predict something useful about data received later) and offers a common way of connecting them together and swapping them out. This makes it a very fruitful sandbox for experimentation and rapid prototyping, with a very easy path to using the same code in production once it’s working well.
