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12-interpretable-ml.Rmd

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 A great advantage of machine learning models is that they can capture non-linear relationships and interactions between predictors, and that they are effective at making use of large data volumes for learning even faint but relevant patterns thanks to their flexibility (high variance). However, their flexibility, and thus complexity, comes with the trade-off that models are hard to interpret. They are essentially black-box models - we know what goes in and we know what comes out and we can make sure that predictions are reliable (as described in previous chapters). However, we don't understand what the model learned. In contrast, a linear regression model can be easily interpreted by looking at the fitted coefficients and their statistics. 
 
-This motivates *interpretable machine learning*. There are two types of model interpretation methods: model-specific and model-agnostic interpretation. A simple example for a model-specific interpretation method is to compare the *t*-values of the fitted coefficients in a least squares linear regression model. Here, we will focus on the model-agnostic machine learning model interpretation and cover two types of model interpretations: quantifying variable importance, and determining partial dependencies (functional relationships between the target variable and a single predictor, while all other predictors are held constant).
+This motivates *interpretable machine learning*. There are two types of model interpretation methods: model-specific and model-agnostic interpretation. A simple example for a model-specific interpretation method is to compare the *t*-values of the fitted coefficients in a least squares linear regression model. This only works for linear regression since this particular statistic cannot be computed in the context of another model type. Model-agnostic methods, in contrast, are not (as the name suggests) specific to certain model types but are algorithmically determined whereby model fitting and prediction are steps of the algorithm and can be performed with any model type. Here, we will focus on model-agnostic interpretable machine learning and cover two types of model interpretations: quantifying variable importance, and determining partial dependencies (that is, functional relationships between the target variable and a single predictor).
 
 We re-use the Random Forest model object which we created in Chapter \@ref(randomforest). As a reminder, we predicted GPP from different environmental variables such as temperature, short-wave radiation, vapor pressure deficit, and others.
 
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 ## Variable importance
 
-A model-agnostic way to quantify variable importance is to permute (shuffle) the values of an individual predictor, re-train the model, and measure by how much the skill of the re-trained model has degraded in comparison to the model trained on the un-manipulated data. The metric, or loss function, for quantifying the model degradation can be any suitable metric for the respective model type. For a model predicting a continuous variable, we may use the RMSE. The algorithm works as follows (taken from [Boehmke & Greenwell (2019)](https://bradleyboehmke.github.io/HOML/iml.html#partial-dependence)):
+A model-agnostic way to quantify variable importance is to permute (shuffle) the values of an individual predictor, re-train the model, and measure by how much the skill of the re-trained model has degraded in comparison to the model trained on the un-manipulated data. Note that by permuting the values of the predictor variable, any association between that predictor and the target variable is broken. The metric, or loss function, for quantifying the model degradation can be any suitable metric for the respective model type. For a model predicting a continuous variable, we may use the RMSE. The algorithm works as follows (taken from [Boehmke & Greenwell (2019)](https://bradleyboehmke.github.io/HOML/iml.html#partial-dependence)):
 
 <!-- Permuting an important variable with random values will destroy any relationship between that variable and the response variable. The model's performance given by a loss function, e.g. its RMSE, will be compared between the non-permuted and permuted model to assess how influential the permuted variable is. A variable is considered to be important, when its permutation increases the model error relative to other variables. Vice versa, permuting an unimportant variable does not lead to a (strong) increase in model error. -->