We trained the most accurate models regardless of their transparency level on subsequent non-overlapping temporal cohorts and extracted faithful decision trees from the models as global surrogate explanations.
How could the central limit theorem apply to making business decisions? Provide an example. The objective function always includes all of the decision variables, but that is not necessarily true of the constraints. Explain the difference between the objective function and the constra...
Forest harvesting is described as cutting trees and distributing them to wood -refining plant(s). To establish the particular parts of the forest to be harvested, it might take several years to decide. This ensures that forest harvesting activities are conducted to be compatible with safegu...
In the next section, we will go through how to implement the PDP and ICE techniques using the Partial Dependence Plot with AutoML (Regression) workflow as an example. Figure 3: The Partial Dependence Plot with AutoML (Regression) workflow, available on the KNIME Hub. We choose “Big Mart ...
The DNN model for this example imitates an automotive lane keeping assist (LKA) system implemented using model predictive control (MPC). A vehicle (ego car) equipped with an LKA system has a sensor, such as camera, that measures the lateral deviation and relative yaw angle between the centerli...
Note that pattern based decision may coincide with reward-oriented behaviour, for example choosing the same action over and over when one option is much better than others25. Finally, the prevalence of reward-oblivious patterns may also be related to the lack of performance-based monetary ...
Let's fit an Explainable Boosting Machine frominterpret.glassboximportExplainableBoostingClassifierebm=ExplainableBoostingClassifier()ebm.fit(X_train,y_train)# or substitute with LogisticRegression, DecisionTreeClassifier, RuleListClassifier, ...# EBM supports pandas dataframes, numpy arrays, and handles ...
Which of the following is the 4th step in analyzing problems with decision trees? A. Estimate payoffs for each possible combination of decision alternatives and states of nature. B. Structure or draw the decision tree. C. solve the ...
Deep learning example with GradientExplainer (TensorFlow/Keras/PyTorch models) Expected gradients combines ideas fromIntegrated Gradients, SHAP, andSmoothGradinto a single expected value equation. This allows an entire dataset to be used as the background distribution (as opposed to a single reference ...
Explain the concepts of error and uncertainty as it relates to decision making. What is the key difference between parametric and nonparametric procedures? What are scales of measurement? Illustrate each scale of measurement with an example. Why are they important to understand?