Designing a simple logistic regression algorithm from scratch, without the use of external ML libraries.
(current version: 0.1)
- Contains version-0.1 of the main program which tries to implement a logistic regression algorithm
- The core of the algorithm is based on an intuitive implementation of basic gradient descent combined with a non-linear function (sigmoid)
- The current version of the codebase is functional, however, future versions may be OOP-based
- Simple plotting functionality is included to visualize the results of the training
Figure showing the dataset with the final classifier (left) and the mean log loss error per epoch (right)
- Converting the codebase to an OOP architecture
- Inclusion of additional classifier training features such as 'strict_reduce' and 'descent_type'
- Advancing the graphing/plotting functionality to include intermediate perceptrons
- Increasing the verbosity of the in-code documentation