Optimizing-ML-models

The project implements the following points for ML models:

  • train and evaluate models using both the two-way holdout method, and an evaluation approach appropriate for models with hyperparameters that uses k-fold cross validation plus a test set.

  • optimize a machine learning model. In particular, using gradient descent to optimize a logistic regression model.

  • Perform optimization with a different algorithm (Newton-Raphson)