So what happens if your ML model is obviously flawed? I.E. it predicts the flip of a coin to always be heads, etc. One potential problem is that the model parameters need to be tweaked from its defaults. A way to fix this problem is to utilize a grid search, in which we test multiple combinations of parameters to see which produce the most accurate results. In this example we’re utilizing a breast cancer dataset already present in the scikit learn library. We’re going to predict if values fall in the ‘target’ field or not (present as a binary 0 or 1).