When designing and implementing Microsoft Azure AI solutions for your exams, interpreting model responses is a crucial skill. Model interpretation is a process of explaining and understanding how machine learning models make decisions. It is often said, “With great power, comes great responsibility.” As we build complex models, we have a responsibility to explain the predictions they provide, for the sake of transparency, fairness, and boosting user acceptance.
Understanding model responses
Understanding model responses helps us in various aspects like model debugging, fairness evaluation, privacy evaluation, and most importantly, improving the model. In the context of Azure AI, there are multiple tools available to interpret the model responses.
Microsoft’s InterpretML is one of them. It is an open-source package for training interpretable models and explaining black box systems. Data Driven Insights (DDI), another important feature of Azure Machine Learning service, offers the interpretation of the trained model automatically.
Example
Now, let’s take an example, considering a building model to predict the outcome of a patient managing diabetes. It takes several features like Age, BMI, Blood Pressure, Number of Pregnancies, etc. The model gives out an output (Prediction) but it’s important to know which feature influenced the prediction the most. This is a typical use case of how model interpretation helps in reality.
With Azure’s InterpretML
With Azure’s InterpretML, you can use different explainers for the same model to understand which features are having the most effect on the outputs. Let’s take a use case using Azure’s InterpretML in Python.
from interpret.ext.blackbox import TabularExplainer
# "features" and "classes" fields are optional
tabular_explainer = TabularExplainer(model,
initialization_examples,
features=feature_names,
classes=classes)
# you can use the training data or test data here
global_tabular_explanation = tabular_explainer.explain_global(x_train)
# sorted feature importance values and feature names
sorted_global_importance_values = global_tabular_explanation.get_ranked_global_values()
sorted_global_importance_names = global_tabular_explanation.get_ranked_global_names()
In the above example, we define a tabular explainer for the model. We then generate a global explanation. Once generated, you can retrieve sorted feature importance values and names.
Similarly, you can generate local feature importance by:
# explain the first member of the test set
instance_num = 1
local_tabular_explanation = tabular_explainer.explain_local(x_test[:instance_num])
# get the prediction for the first member of the test set and explain why model made that prediction
prediction_value = clf.predict(x_test)[instance_num]
sorted_local_importance_values = local_tabular_explanation.get_ranked_local_values()[prediction_value]
sorted_local_importance_names = local_tabular_explanation.get_ranked_local_names()[prediction_value]
The interpretation of model responses
The interpretation of model responses not only helps in understanding the decision process of the AI model but also in uncovering any bias in the model. For instance, if in the diabetes prediction, the model is giving more weightage to gender than medical parameters, it indicates a gender bias in the model.
In conclusion
In conclusion, interpreting model responses for AI solutions is an imperative step. In the context of Microsoft Azure, their rich set of tools provides a great platform and opportunity for data scientists to interpret their AI models and help build more substantial and fair solutions.
Practice Test
True or False: You cannot create a custom vision model in Azure AI.
- False.
Correct Answer: False
Explanation: You can indeed create a custom vision model in Azure. This is one of the features offered by the Azure AI platform.
In Azure AI, the model responses for a text analytics API are in the format of:
- a. JSON
- b. XML
- c. CSV
- d. Excel
Answer: a. JSON
Explanation: The Text Analytics API returns a JSON document that contains the results of the analysis.
True or False: The sentiment score in Azure AI text analytics ranges from -1 to
- False.
Correct Answer: False
Explanation: The sentiment score in Azure AI text analytics ranges from 0 to 1, where 0 indicates negative sentiment and 1 indicates positive sentiment.
Interpreting model responses is important for:
- a. Understanding model output
- b. Deriving actionable insight
- c. Verifying the accuracy of the model predictions
- d. All of the above
Answer: d. All of the above
Explanation: Interpreting model responses helps in understanding the model outputs, provides insights for decision making, and helps in verifying the accuracy of the model’s predictions.
In Azure computer vision API, an “adult” value of “true” in the model response means:
- The image contains adult content.
Answer: The image contains adult content.
Explanation: An ‘adult’ value of ‘true’ indicates that the image is detected to contain adult content.
Which of these can help improve the accuracy of your Azure AI model?
- a. Increasing the size of the training data set
- b. Using more complex models
- c. Regularly retraining the model
- d. All of the above
Answer: d. All of the above
Explanation: A larger training data set, a more complex model, and regular retraining can all contribute to increased accuracy.
True or False: A key phrase in Azure AI’s Text Analytics API refers to the main points in a sentence or document.
- True.
Correct Answer: True
Explanation: Key phrases are the main talking points in an input text, providing a summary of its contents.
Are phrases like “the best” and “perform well” considered positive sentiment in Azure AI?
- Yes
Answer: Yes
Explanation: Phrases like “the best” and “perform well” typically indicate a positive sentiment in text analytics.
True or False: The output of Azure’s face API includes information about gender, age, and emotion.
- True.
Correct Answer: True
Explanation: The Face API in Azure AI can detect and analyze faces in images, providing information such as gender, age, emotion, and more.
What does the ‘tags’ element in an Azure Computer Vision API model response typically contain?
- Descriptions of key objects in the image.
Answer: Descriptions of key objects in the image.
Explanation: The ‘tags’ element usually contains objects or things that are present in the image.
In Azure Form Recognizer API, a value score close to 1 for a particular field means:
- a. The confidence for the field value is low.
- b. The confidence for the field value is high.
- c. The field value is incorrect.
- d. The field value is unknown.
Answer: b. The confidence for the field value is high.
Explanation: In Form Recognizer, the value score corresponds to confidence and a score close to 1 indicates high confidence.
What does the ‘objects’ element in an Azure Custom Vision API model response typically contain?
- a. The objects detected in the image.
- b. The emotions expressed in the image.
- c. The background color of the image.
- d. The quality of the image.
Answer: a. The objects detected in the image.
Explanation: The ‘objects’ element typically contains the objects that were detected in the image. Each object is represented by its type and location in the image.
True or False: Interpreting model responses is not necessary once the model is deployed.
- False.
Correct Answer: False
Explanation: Once the model is deployed, interpreting model responses is still necessary to ensure the accuracy of the model’s predictions and to make informed decisions based on these predictions.
In Azure Speech service, the output format of the model response can be:
- a. Simple
- b. Detailed
- c. Either simple or detailed
- d. Neither simple nor detailed
Answer: c. Either simple or detailed
Explanation: Azure Speech service allows you to choose between a simple or detailed output format. The ‘simple’ format provides basic information – just the recognized phrase. The ‘detailed’ format includes more detailed information, like the timings for when words were spoken and confidence scores.
True or False: In Azure AI, the interpretation of a model response generally depends on the type of model and the service used.
- True.
Correct Answer: True
Explanation: Different types of models and services return different types of responses. Therefore, the interpretation will generally depend on these factors.
Interview Questions
What is the purpose of interpretation in Machine Learning Model response?
The purpose of interpretation in a Machine Learning Model response is to understand the relationships and dependencies between input features and the model output. It helps to explain the predictions made by the model, making the model more transparent and trusted.
What is Microsoft’s LIME for interpreting Model Responses?
LIME (Local Interpretable Model-Agnostic Explanations) is a tool used by Microsoft for understanding and explaining machine learning models. It explains the predictions of any machine learning classifier in an interpretable and faithful manner.
What is an explainer in Azure’s Model Interpretability toolkit?
An explainer is a module in Azure’s Model Interpretability toolkit that helps to interpret the model’s predictions. They highlight the impact of different features on the prediction made by the model.
What is the use of Feature Importance during model interpretation in Azure AI?
Feature importance deals with quantifying the impact that each feature has on the predicted outcome. It gives an understanding of which features are contributing most to the predictions.
Describe the process of implementing model response interpretability in Azure AI.
The process includes training a model using Azure Machine Learning, running the explainer for the model by specifying it, the training data, and the model itself, and finally visualizing the global and local feature importance.
How do SHAP values assist with interpreting Model Responses?
SHAP (SHapley Additive exPlanations) values provide a cohesive and unified measure of feature importance, and provide insights into how much each feature contributes to the prediction for each individual instance.
How does Azure AI use counterfactual explanations in interpreting model responses?
Counterfactual explanations in Azure AI describe the smallest change to the feature values that change the prediction outcome. This helps to understand what factors could lead to a different prediction.
What is the purpose of model interpretability in Azure AI?
The purpose of model interpretability in Azure AI is to promote transparency, debugging, fairness, and trustworthiness of the predictions made by the AI models.
What does the PDP (Partial Dependence Plot) provide during model interpretation?
A Partial Dependence Plot (PDP) shows the marginal effect of one or two features on the predicted outcome of a machine learning model. This helps to visualize the effect of certain features on the model prediction.
How can we interpret machine learning models using Azure Machine Learning designer?
Azure Machine Learning designer provides a visual interface to build, test, and deploy machine learning models. Through this, you can interpret the model by analyzing the weightage or contribution of individual variables to the training of the model.
When are global explainers used in Azure’s Model Interpretability toolkit?
Global explainers are used when you need to understand the model as a whole, for summarizing insights about the model.
What are local explanations?
Local explanations focus on a single prediction made by the model, explaining why the model made that prediction.
What is a cognitive service in Microsoft Azure AI?
Cognitive Services in Azure AI are prebuilt AI services that provide APIs, customizability, and tools to deploy models that continually learn from your specific data.
What are model-agnostic methods in Azure AI model interpretability?
Model-agnostic methods in Azure AI are interpretation techniques that can be applied to any machine learning model, providing insights about the feature importance or contribution to the result.
What is the relationship between feature importance and model interpretability?
The feature importance is a key aspect of model interpretability. The greater the importance of a feature, the more that feature influences the model’s predictions. This understanding forms the basis for interpreting how the model works.
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