This technique is broadly-applicable because it doesn't rely on internal model parameters, such as linear regression coefficients (which are really just poor proxies for feature importance). It directly measures variable importance by observing the effect on model accuracy of randomly shuffling each predictor variable. Permutation importance is a common, reasonably efficient, and very reliable technique. In fact, the RF importance technique we'll introduce here ( permutation importance) is applicable to any model, though few machine learning practitioners seem to realize this. Most random Forest (RF) implementations also provide measures of feature importance. Feature importance is the most useful interpretation tool, and data scientists regularly examine model parameters (such as the coefficients of linear models), to identify important features.įeature importance is available for more than just linear models. For example, if you build a model of house prices, knowing which features are most predictive of price tells us which features people are willing to pay for. Training a model that accurately predicts outcomes is great, but most of the time you don't just need predictions, you want to be able to interpret your model. Epilogue: Explanations and Further Possibilities.Breast cancer data set multi-collinearities.The effect of collinear features on importance.The effect of validation set size on importance.Comparing R to scikit-learn importances.In addition, your feature importance measures will only be reliable if your model is trained with suitable hyper-parameters. For R, use importance=T in the Random Forest constructor then type=1 in R's importance() function. To get reliable results in Python, use permutation importance, provided here and in our rfpimp package (via pip). The scikit-learn Random Forest feature importance and R's default Random Forest feature importance strategies are biased. Updated Apto include many more experiments in the Experimental results section. Updated Apto include new rfpimp package features to handle collinear dataframe columns in Dealing with collinear features section. See new section Breast cancer data set multi-collinearities. Updated all plots and section Dealing with collinear features. Update Octoto show better feature importance plot and a new feature dependence heatmap. scikit-learn just merged an implementation of permutation importance. It's based upon a technique that computes Partial Dependence through Stratification. Wilson and Jeff Hamrick) just released Nonparametric Feature Impact and Importance that doesn't require a user's fitted model to compute impact. Kerem and Christopher are current MS Data Science students.) For more material, see Jeremy's fast.ai courses. You might know Terence as the creator of the ANTLR parser generator. (Terence is a tech lead at Google and ex-Professor of computer/data science both he and Jeremy teach in University of San Francisco's MS in Data Science program.
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