In 1993 Entropic Research Laboratory Inc. Where it has been used to build CUED's large vocabulary speech Of the Cambridge University Engineering Department (CUED) (formerly known as the Speech Vision and Robotics Group) HTK was originally developed at the Machine Intelligence Laboratory The HTK release contains extensive documentation and examples. Supports HMMs using both continuous density mixture Gaussians andĭiscrete distributions and can be used to build complex HMM systems. The tools provide sophisticated facilities for speechĪnalysis, HMM training, testing and results analysis. HTK consists of a set of library modules and tools available in C Other applications including research into speech synthesis, character HTK is primarily usedįor speech recognition research although it has been used for numerous The performance of the black box model does not play a role in training the surrogate model.The Hidden Markov Model Toolkit (HTK) is a portable toolkit forīuilding and manipulating hidden Markov models. Note that we have not talked about the model performance of the underlying black box model, i.e. how good or bad it performs in predicting the actual outcome. If the R-squared is close to 0 (= high SSE), then the interpretable model fails to explain the black box model. If the interpretable model is very close, you might want to replace the complex model with the interpretable model. If R-squared is close to 1 (= low SSE), then the interpretable model approximates the behavior of the black box model very well. The R-squared measure can be interpreted as the percentage of variance that is captured by the surrogate model. SSE stands for sum of squares error and SST for sum of squares total. \[g(x)=\beta_0+\beta_1\) the mean of the black box model predictions. We want to approximate our black box prediction function f as closely as possible with the surrogate model prediction function g, under the constraint that g is interpretable.įor the function g any interpretable model – for example from the interpretable models chapter – can be used. There is actually not much theory needed to understand surrogate models. The idea of surrogate models can be found under different names:Īpproximation model, metamodel, response surface model, emulator, … The purpose of (interpretable) surrogate models is to approximate the predictions of the underlying model as accurately as possible and to be interpretable at the same time. The difference between the surrogate models used in engineering and in interpretable machine learning is that the underlying model is a machine learning model (not a simulation) and that the surrogate model must be interpretable. If an outcome of interest is expensive, time-consuming or otherwise difficult to measure (e.g. because it comes from a complex computer simulation), a cheap and fast surrogate model of the outcome can be used instead. Surrogate models are also used in engineering: 10.5.4 Disadvantages of Identifying Influential Instances.10.5.3 Advantages of Identifying Influential Instances.10.3.5 Bonus: Other Concept-based Approaches.10.3.1 TCAV: Testing with Concept Activation Vectors.10.2.1 Vanilla Gradient (Saliency Maps).9.6 SHAP (SHapley Additive exPlanations).9.3.1 Generating Counterfactual Explanations.9.1 Individual Conditional Expectation (ICE).8.5.2 Should I Compute Importance on Training or Test Data?.8.4.5 Generalized Functional ANOVA for Dependent Features.8.4.3 How not to Compute the Components II.8.4.1 How not to Compute the Components I.8.2 Accumulated Local Effects (ALE) Plot.5.5.1 Learn Rules from a Single Feature (OneR).5.2.1 What is Wrong with Linear Regression for Classification?.5.1.6 Do Linear Models Create Good Explanations?.4.3 Risk Factors for Cervical Cancer (Classification).4.2 YouTube Spam Comments (Text Classification). ![]() 3.3.5 Local Interpretability for a Group of Predictions.3.3.4 Local Interpretability for a Single Prediction.3.3.3 Global Model Interpretability on a Modular Level.3.3.2 Global, Holistic Model Interpretability.3.2 Taxonomy of Interpretability Methods.
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