# Interpreting a Model¶

There are two methods you can use for interpreting models:

• Using the Interpret this Model button on a completed experiment page to interpret a Driverless AI model.
• Using the MLI link in the upper right corner of the UI to interpret either a Driverless AI model or an external model.

## Interpret this Model Button¶

After an experiment status changes from RUNNING to COMPLETE, the UI provides you with several options:

• Interpret this Model on Original Features
• Interpret this Model on Transformed Features
• Score on Another Dataset (Refer to Score on Another Dataset.)
• Transform Another Dataset (Refer to Transform Another Dataset.)
• Download (Holdout) Training Predictions (in csv format, available if a validation set was NOT provided)
• Download Validation Predictions (in csv format, available if a validation set was provided)
• Download Test Predictions (in csv format, available if a test dataset is used)
• Download Python Scoring Pipeline (A standalone Python scoring pipeline for H2O Driverless AI. Refer to Driverless AI Standalone Python Scoring Pipeline.)
• Download MOJO Scoring Pipeline (A standalone Model Object, Optimized scoring pipeline. Refer to Driverless AI MOJO Scoring Pipeline.)
• Download Experiment Summary (a zip file containing a summary of the experiment and the features along with their relative importance)
• View Notifications/Warnings (if any existed)

Click one of the Interpret this Model buttons to launch the Model Interpretation page. This page provides several visual explanations of the trained Driverless AI model and its results.

## Model Interpretation on Driverless AI Models¶

This method allows you to run model interpretation on a Driverless AI model. This method is similar to clicking “Interpret This Model” on an experiment summary page.

1. Click the MLI link in the upper-right corner of the UI to view a list of interpreted models.
1. Click the New Interpretation button.
2. Select the dataset that was used to train the model that you will use for interpretation.
3. Specify the Driverless AI model that you want to use for the interpretation.
4. Specify the column of the target variable (the column of actuals for MLI).
5. Optionally specify weight and dropped columns.
6. Optionally specify a clustering column and whether to use the original features.
7. Optionally specify the number of cross-validation folds to use in k-LIME. This defaults to 0, and the maximum value is 10.
8. Click the Launch MLI button.

## Model Interpretation on External Models¶

Model Interpretation does not need to be run on a Driverless AI experiment. You can train an external model and run Model Interpretability on the predictions.

1. Click the MLI link in the upper-right corner of the UI to view a list of interpreted models.
1. Click the New Interpretation button.

2. Select the dataset that you want to use for the model interpretation. This must include a prediction column that was generated by the external model. If the dataset does not have predictions, then you can join the external predictions. An example showing how to do this in Python is available in the Run Model Interpretation on External Model Predictions section.

Note: When running interpretations on an external model, leave the Select Model option empty. That option is for selecting a Driverless AI model.

3. Specify a Target Column (actuals) and the Prediction Column (scores from the model).

4. Optionally specify weight and dropped columns.

5. Optionally specify a clustering column.

6. Optionally specify the number of cross-validation folds to use in k-LIME. This defaults to 0, and the maximum value is 10.

7. Click the Launch MLI button.

## The Model Interpretation Page¶

The Model Interpretation page includes the following information:

• Global interpretable model explanation plot
• Feature importance (Global for original features; Shapley for transformed features; LOCO for interpretations with predictions and when interpreting on raw features)
• Decision tree surrogate model
• Partial dependence and individual conditional expectation plots

Each of these plots and techniques provide different types of insights and explanations regarding a model and its results.

## K-LIME¶

### The K-LIME Technique¶

K-LIME is a variant of the LIME technique proposed by Ribeiro at al (2016). K-LIME generates global and local explanations that increase the transparency of the Driverless AI model, and allow model behavior to be validated and debugged by analyzing the provided plots, and comparing global and local explanations to one-another, to known standards, to domain knowledge, and to reasonable expectations.

K-LIME creates one global surrogate GLM on the entire training data and also creates numerous local surrogate GLMs on samples formed from k-means clusters in the training data. All penalized GLM surrogates are trained to model the predictions of the Driverless AI model. The number of clusters for local explanations is chosen by a grid search in which the $$R^2$$ between the Driverless AI model predictions and all of the local K-LIME model predictions is maximized. The global and local linear model’s intercepts, coefficients, $$R^2$$ values, accuracy, and predictions can all be used to debug and develop explanations for the Driverless AI model’s behavior.

The parameters of the global K-LIME model give an indication of overall linear feature importance and the overall average direction in which an input variable influences the Driverless AI model predictions. The global model is also used to generate explanations for very small clusters ($$N < 20$$) where fitting a local linear model is inappropriate.

The in-cluster linear model parameters can be used to profile the local region, to give an average description of the important variables in the local region, and to understand the average direction in which an input variable affects the Driverless AI model predictions. For a point within a cluster, the sum of the local linear model intercept and the products of each coefficient with their respective input variable value are the K-LIME prediction. By disaggregating the K-LIME predictions into individual coefficient and input variable value products, the local linear impact of the variable can be determined. This product is sometimes referred to as a reason code and is used to create explanations for the Driverless AI model’s behavior.

In the following example, reason codes are created by evaluating and disaggregating a local linear model.

Given the row of input data with its corresponding Driverless AI and K-LIME predictions:

debt_to_income_ ratio credit_ score savings_acct_ balance observed_ default H2OAI_predicted_ default K-LIME_predicted_ default
30 600 1000 1 0.85 0.9

And the local linear model:

$$\small{y_\text{K-LIME} = 0.1 + 0.01 * debt\_to\_income\_ratio + 0.0005 * credit\_score + 0.0002 * savings\_account\_balance}$$

It can be seen that the local linear contributions for each variable are:

• debt_to_income_ratio: 0.01 * 30 = 0.3
• credit_score: 0.0005 * 600 = 0.3
• savings_acct_balance: 0.0002 * 1000 = 0.2

Each local contribution is positive and thus contributes positively to the Driverless AI model’s prediction of 0.85 for H2OAI_predicted_default. By taking into consideration the value of each contribution, reason codes for the Driverless AI decision can be derived. debt_to_income_ratio and credit_score would be the two largest negative reason codes, followed by savings_acct_balance.

The local linear model intercept and the products of each coefficient and corresponding value sum to the K-LIME prediction. Moreover it can be seen that these linear explanations are reasonably representative of the nonlinear model’s behavior for this individual because the K-LIME predictions are within 5.5% of the Driverless AI model prediction. This information is encoded into English language rules which can be viewed by clicking the Explanations button.

Like all LIME explanations based on linear models, the local explanations are linear in nature and are offsets from the baseline prediction, or intercept, which represents the average of the penalized linear model residuals. Of course, linear approximations to complex non-linear response functions will not always create suitable explanations and users are urged to check the K-LIME plot, the local model $$R^2$$, and the accuracy of the K-LIME prediction to understand the validity of the K-LIME local explanations. When K-LIME accuracy for a given point or set of points is quite low, this can be an indication of extremely nonlinear behavior or the presence of strong or high-degree interactions in this local region of the Driverless AI response function. In cases where K-LIME linear models are not fitting the Driverless AI model well, nonlinear LOCO feature importance values may be a better explanatory tool for local model behavior. As K-LIME local explanations rely on the creation of k-means clusters, extremely wide input data or strong correlation between input variables may also degrade the quality of K-LIME local explanations.

### The Global Interpretable Model Explanation Plot¶

This plot is in the upper-left quadrant of the UI. It shows Driverless AI model predictions and K-LIME model predictions in sorted order by the Driverless AI model predictions. This graph is interactive. Hover over the Model Prediction, K-LIME Model Prediction, or Actual Target radio buttons to magnify the selected predictions. Or click those radio buttons to disable the view in the graph. You can also hover over any point in the graph to view K-LIME reason codes for that value. By default, this plot shows information for the global K-LIME model, but you can change the plot view to show local results from a specific cluster. The K-LIME plot also provides a visual indication of the linearity of the Driverless AI model and the trustworthiness of the K-LIME explanations. The closer the local linear model approximates the Driverless AI model predictions, the more linear the Driverless AI model and the more accurate the explanation generated by the K-LIME local linear models.

## Global and Local Feature Importance¶

Feature importance measures the effect that a feature has on the predictions of a model. Global and local feature importance values enable increased transparency in the Driverless AI model and enable validating and debugging of the Driverless AI model by comparing global model behavior to the local model behavior, and by comparing to global and local feature importance to known standards, domain knowledge, and reasonable expectations.

### Global Feature Importance Technique¶

Global feature importance measures the overall impact of an input feature on the Driverless AI model predictions while taking nonlinearity and interactions into consideration. Global feature importance values give an indication of the magnitude of a feature’s contribution to model predictions for all rows. Unlike regression parameters, they are often unsigned and typically not directly related to the numerical predictions of the model. The reported global feature importance values are calculated by aggregating the improvement in the split-criterion for a feature across all the trees in an ensemble. The aggregated feature importance values are then scaled between 0 and 1, such that the most important feature has an importance value of 1.

You can view a global feature importance plot by selecting the Interpret this Model on Original Features button in an experiment.

### Local Feature Importance Technique¶

Local feature importance describes how the combination of the learned model rules or parameters and an individual row’s attributes affect a model’s prediction for that row while taking nonlinearity and interactions into effect. Local feature importance values reported here are based on a variant of the leave-one-covariate-out (LOCO) method (Lei et al, 2017).

In the LOCO-variant method, each local feature importance is found by re-scoring the trained Driverless AI model for each feature in the row of interest, while removing the contribution to the model prediction of splitting rules that contain that feature throughout the ensemble. The original prediction is then subtracted from this modified prediction to find the raw, signed importance for the feature. All local feature importance values for the row are then scaled between 0 and 1 for direct comparison with global feature importance values.

Given the row of input data with its corresponding Driverless AI and K-LIME predictions:

debt_to_income_ ratio credit_ score savings_acct_ balance observed_ default H2OAI_predicted_ default K-LIME_predicted_ default
30 600 1000 1 0.85 0.9

Taking the Driverless AI model as F(X), LOCO-variant feature importance values are calculated as follows.

First, the modified predictions are calculated:

$$F_{~debt\_to\_income\_ratio} = F(NA, 600, 1000) = 0.99$$

$$F_{~credit\_score} = F(30, NA, 1000) = 0.73$$

$$F_{~savings\_acct\_balance} = F(30, 600, NA) = 0.82$$

Second, the original prediction is subtracted from each modified prediction to generate the unscaled local feature importance values:

$$\text{LOCO}_{debt\_to\_income\_ratio} = F_{~debt\_to\_income\_ratio} - 0.85 = 0.99 - 0.85 = 0.14$$

$$\text{LOCO}_{credit\_score} = F_{~credit\_score} - 0.85 = 0.73 - 0.85 = -0.12$$

$$\text{LOCO}_{savings\_acct\_balance} = F_{~savings\_acct\_balance} - 0.85 = 0.82 - 0.85 = -0.03$$

Finally LOCO values are scaled between 0 and 1 by dividing each value for the row by the maximum value for the row and taking the absolute magnitude of this quotient.

$$\text{Scaled}(\text{LOCO}_{debt\_to\_income\_ratio}) = \text{Abs}(\text{LOCO}_{~debt\_to\_income\_ratio}/0.14) = 1$$

$$\text{Scaled}(\text{LOCO}_{credit\_score}) = \text{Abs}(\text{LOCO}_{~credit\_score}/0.14) = 0.86$$

$$\text{Scaled}(\text{LOCO}_{savings\_acct\_balance}) = \text{Abs}(\text{LOCO}_{~savings\_acct\_balance} / 0.14) = 0.21$$

One drawback to these LOCO-variant feature importance values is, unlike K-LIME, it is difficult to generate a mathematical error rate to indicate when LOCO values may be questionable.

LOCO feature importance plots are available for new interpretations with predictions (without a model) and for new interpretations run using the Interpret on Raw Features button.

### Shapley Explanations¶

Shapley explanations are a technique with credible theoretical support that presents consistent global and local variable contributions. Local numeric Shapley values are calculated by tracing single rows of data through a trained tree ensemble and aggregating the contribution of each input variable as the row of data moves through the trained ensemble.

For regression tasks, Shapley values should sum to the prediction of the Driverless AI model. For classification problems, Shapely values should sum to the prediction of the Driverless AI model before applying the link function. Global Shapley values are the average of the local Shapley values over every row of a data set.

You can view a Shapley explanations plot by selecting the Interpret this Model on Transformed Features button in an experiment.

## Decision Tree Surrogate Model¶

### The Decision Tree Surrogate Model Technique¶

The decision tree surrogate model increases the transparency of the Driverless AI model by displaying an approximate flow-chart of the complex Driverless AI model’s decision making process. The decision tree surrogate model also displays the most important variables in the Driverless AI model and the most important interactions in the Driverless AI model. The decision tree surrogate model can be used for visualizing, validating, and debugging the Driverless AI model by comparing the displayed decision-process, important variables, and important interactions to known standards, domain knowledge, and reasonable expectations.

A surrogate model is a data mining and engineering technique in which a generally simpler model is used to explain another, usually more complex, model or phenomenon. The decision tree surrogate is known to date back at least to 1996 (Craven and Shavlik). The decision tree surrogate model here is trained to predict the predictions of the more complex Driverless AI model using the of original model inputs. The trained surrogate model enables a heuristic understanding (i.e., not a mathematically precise understanding) of the mechanisms of the highly complex and nonlinear Driverless AI model.

### The Decision Tree Surrogate Model Plot¶

The lower-left quadrant shows a decision tree surrogate for the generated model. The highlighted row shows the path to the highest probability leaf node and indicates the globally important variables and interactions that influence the Driverless AI model prediction for that row.

## Partial Dependence and Individual Conditional Expectation (ICE)¶

### The Partial Dependence Technique¶

Partial dependence is a measure of the average model prediction with respect to an input variable. Partial dependence plots display how machine-learned response functions change based on the values of an input variable of interest, while taking nonlinearity into consideration and averaging out the effects of all other input variables. Partial dependence plots are well-known and described in the Elements of Statistical Learning (Hastie et al, 2001). Partial dependence plots enable increased transparency in Driverless AI models and the ability to validate and debug Driverless AI models by comparing a variable’s average predictions across its domain to known standards, domain knowledge, and reasonable expectations.

### The ICE Technique¶

Individual conditional expectation (ICE) plots, a newer and less well-known adaptation of partial dependence plots, can be used to create more localized explanations for a single individual using the same basic ideas as partial dependence plots. ICE Plots were described by Goldstein et al (2015). ICE values are simply disaggregated partial dependence, but ICE is also a type of nonlinear sensitivity analysis in which the model predictions for a single row are measured while a variable of interest is varied over its domain. ICE plots enable a user to determine whether the model’s treatment of an individual row of data is outside one standard deviation from the average model behavior, whether the treatment of a specific row is valid in comparison to average model behavior, known standards, domain knowledge, and reasonable expectations, and how a model will behave in hypothetical situations where one variable in a selected row is varied across its domain.

Given the row of input data with its corresponding Driverless AI and K-LIME predictions:

debt_to_income_ ratio credit_ score savings_acct_ balance observed_ default H2OAI_predicted_ default K-LIME_predicted_ default
30 600 1000 1 0.85 0.9

Taking the Driverless AI model as F(X), assuming credit scores vary from 500 to 800 in the training data, and that increments of 30 are used to plot the ICE curve, ICE is calculated as follows:

$$\text{ICE}_{credit\_score, 500} = F(30, 500, 1000)$$

$$\text{ICE}_{credit\_score, 530} = F(30, 530, 1000)$$

$$\text{ICE}_{credit\_score, 560} = F(30, 560, 1000)$$

$$...$$

$$\text{ICE}_{credit\_score, 800} = F(30, 800, 1000)$$

The one-dimensional partial dependence plots displayed here do not take interactions into account. Large differences in partial dependence and ICE are an indication that strong variable interactions may be present. In this case partial dependence plots may be misleading because average model behavior may not accurately reflect local behavior.

### The Partial Dependence and Individual Conditional Expectation Plot¶

Overlaying ICE plots onto partial dependence plots allow the comparison of the Driverless AI model’s treatment of certain examples or individuals to the model’s average predictions over the domain of an input variable of interest.

The lower-right quadrant shows the partial dependence for a selected variable and the ICE values when a specific row is selected. Users may select a point on the graph to see the specific value at that point. By default, this graph shows the partial dependence values for the top feature. Change this view by selecting a different feature in the feature drop-down. Note that this graph is available for the top ten most important original input variables. (Categorical variables with less than 20 unique values are only included if in the top 10.)

## General Considerations¶

### Machine Learning and Approximate Explanations¶

For years, common sense has deemed the complex, intricate formulas created by training machine learning algorithms to be uninterpretable. While great advances have been made in recent years to make these often nonlinear, non-monotonic, and non-continuous machine-learned response functions more understandable (Hall et al, 2017), it is likely that such functions will never be as directly or universally interpretable as more traditional linear models.

Why consider machine learning approaches for inferential purposes? In general, linear models focus on understanding and predicting average behavior, whereas machine-learned response functions can often make accurate, but more difficult to explain, predictions for subtler aspects of modeled phenomenon. In a sense, linear models create very exact interpretations for approximate models. The approach here seeks to make approximate explanations for very exact models. It is quite possible that an approximate explanation of an exact model may have as much, or more, value and meaning than the exact interpretations of an approximate model. Moreover, the use of machine learning techniques for inferential or predictive purposes does not preclude using linear models for interpretation (Ribeiro et al, 2016).

### The Multiplicity of Good Models in Machine Learning¶

It is well understood that for the same set of input variables and prediction targets, complex machine learning algorithms can produce multiple accurate models with very similar, but not exactly the same, internal architectures (Breiman, 2001). This alone is an obstacle to interpretation, but when using these types of algorithms as interpretation tools or with interpretation tools it is important to remember that details of explanations will change across multiple accurate models.

### Expectations for Consistency Between Explanatory Techniques¶

• The decision tree surrogate is a global, nonlinear description of the Driverless AI model behavior. Variables that appear in the tree should have a direct relationship with variables that appear in the global feature importance plot. For certain, more linear Driverless AI models, variables that appear in the decision tree surrogate model may also have large coefficients in the global K-LIME model.
• K-LIME explanations are linear, do not consider interactions, and represent offsets from the local linear model intercept. LOCO importance values are nonlinear, do consider interactions, and do not explicitly consider a linear intercept or offset. LIME explanations and LOCO importance values are not expected to have a direct relationship but can align roughly as both are measures of a variable’s local impact on a model’s predictions, especially in more linear regions of the Driverless AI model’s learned response function.
• ICE is a type of nonlinear sensitivity analysis which has a complex relationship to LOCO feature importance values. Comparing ICE to LOCO can only be done at the value of the selected variable that actually appears in the selected row of the training data. When comparing ICE to LOCO the total value of the prediction for the row, the value of the variable in the selected row, and the distance of the ICE value from the average prediction for the selected variable at the value in the selected row must all be considered.
• ICE curves that are outside the standard deviation of partial dependence would be expected to fall into less populated decision paths of the decision tree surrogate; ICE curves that lie within the standard deviation of partial dependence would be expected to belong to more common decision paths.
• Partial dependence takes into consideration nonlinear, but average, behavior of the complex Driverless AI model without considering interactions. Variables with consistently high partial dependence or partial dependence that swings widely across an input variable’s domain will likely also have high global importance values. Strong interactions between input variables can cause ICE values to diverge from partial dependence values.