As is evident, the accuracy achieved by soft voting is 2% worse than the best learner and on par with the second-best learner. We would like to analyze our results similarly to how we analyzed the performance of our hard voting custom implementation. But as soft voting takes into account the predicted class probabilities, we cannot use the same approach. Instead, we will plot the predicted probability for each instance to be classified as positive by each base learner as well as the average probability of the ensemble.
Again, we import matplotlib and set the plotting style:
# --- SECTION 1 ---# Import the required librariesimport matplotlib as mplimport matplotlib.pyplot as pltmpl.style.use('seaborn-paper')
We calculate ...
