Python machine learning sklearn.linear_model vs custom code
I recommend you use as much as possible the functions given by sklearn or another ML library (I like TensorFlow). That's because it's very difficult to get the performance of any library. They are calculating in a low level of the operating system, meanwhile the common users don't implement the computational actions outside the python environment.
Moreover, python by itself is not very efficient in relation to the data structures. For example,a simple array is implemented as a LinkedList. The ML libraries use Numpy to their computations to get a better performance.
I am new to machine learning and Python. I am trying to understand when to use the functions in sklearn.linear_model (linearregression and logisticregression) and when to implement my own code for the same. Any suggestions or references will be highly appreciated.
regards
Souvik