Explain your machine learning with feature importance

Although it is important to be proficient in understanding the inner workings of the algorithm, it is far more essential to be able to communicate the findings to an audience who may not have any theoretical / practical knowledge of machine learning. Just showing that the algorithm predicts well is not enough. You have to attribute the predictions to the elements of the input data that contribute to your accuracy. Thankfully, the random forest implementation of sklearn does give an output called "feature importances" which helps us explain the predictive power of the features in the dataset. But, there are certain drawbacks to this method that we will explore in this post, and an alternative technique to assess the feature importances that overcomes these drawbacks.