• Fahad Mehfooz

Confidence is necessary as it acts as the base when developing a machine learning model. But is it always reliable? While developing model, different aspects should be considered like security, data for training shouldn’t be flawed, risk management should be handled, etc. By this paper, I would address how someone can build a trustworthy model, what problems should be addressed and how to remove those issues.

Threshold, Class Imbalance, Skewed, Black-box and Correlation.

"In what scenarios are machine learning models safe to apply?", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.394-395, March-2019, Available :http://www.jetir.org/papers/JETIR1903L52.pdf

"In what scenarios are machine learning models safe to apply?", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp394-395, March-2019, Available at : http://www.jetir.org/papers/JETIR1903L52.pdf