Machine Learning on a Cancer Dataset - Part 18 -- Steemit

In the third and final video on Random Forest we're looking at the importance each feature of the cancer sample plays in the decision making process. Recall from previous videos that we've done something similar for Decision Trees. The feature importances matrix was skewed, having 2-3 features (out of 30) play the majority of importance in decision making, while the rest of the features were close to zero. This may not be an accurate model of cancer because it may be least likely to have a feature like'worst radius' play such a'heavy' role in predicting if a tumor is malignant or benign. As we compute the feature importances matrix for the Random Forest classifier we see that it looks much more balanced compared to the one for DT.