13
Sep

As machine learning is increasingly used in applications, machine learning algorithms have gained more scrutiny. With larger data sets, various implementations, algorithms, and learning requirements, it has become even more complex to create and evaluate ML models since all those factors directly impact the overall accuracy and learning outcome of the model. This is further skewed by false assumptions, noise, and outliers. Machine learning models cannot be a black box. The user needs to be fully aware of their data and algorithms to trust the outputs and outcomes. Any issues in the algorithm or polluted data set can negatively impact the ML model. The main…