Machine Learning Model Flow Chart
In applications like insurance or credit screening, a model should be interpretable as it’s essential for the model to avoid inadvertently discriminating against certain clients. In brief, Your models have to be held accountable for output. Formally, the model assumes that we can receive the output value employing a linear mixture of the input values. The last model is tested and then employed for decision-making if it will become the very best performer. No machine learning model is ideal.
When designing a machine learning system it’s important to comprehend how your data will change over time. Data