Question 1

You need to retrieve the primary metric for a regression task. How can you code this in Python?

Question 2

You need to retrieve the best run and its model. How can you code that with the SDK?

best_run, fitted_model = automl.run.get_output() best_run_metrics = best_run.get_metrics() for metric_name in best_run_metrics: metric = best_run_metrics[metric_name] print(metric_name, metric)

best_run, fitted_model = automl_run.get_output() best_run_metrics = best_run_get_metrics(1) for metric_name in best_run_metrics: metric = best_run_metrics[metric_name] print(metric_name, metric)

best_run, fitted_model = automl_run.get_output() best_run_metrics = best_run.get_metrics() for metric_name in best_run_metrics: metric = best_run_metrics[metric_name] print(metric_name, metric)

best_run, fitted_model = automl_run.get_input() best_run_metrics = best_run.get_metrics() for metric_name in best_run_metrics: metric = best_run_metrics[metric_name] print(metric_name, metric)

Question 3

How can you code an instance of a MimicExplainer for a model named loan_model?

from interpret.ext.blackbox import MimicExplainer from interpret.ext.glassbox import DecisionTreeExplainableModel mim_explainer = MimicExplainer(model=loan_model, initialization_examples=X_test, explainable_model = DecisionTreeExplainableModel, features=['loan_amount','income','age','marital_status'], classes=['reject', 'approve'])

from interpret.ext.blackbox import MimicExplainer from interpret.ext.glassbox import DecisionTreeExplainableModel mim_explainer = MimicExplainer(model=loan_model, initialization_examples=X_test, explainable_model = DecisionTree, classes=['loan_amount','income','age','marital_status'], features=['reject', 'approve'])

from interpret.ext.blackbox import MimicExplainer from interpret.ext.glassbox import DecisionTreeExplainableModel mim_explainer = MimicExplainer(model=loan_model, explainable_model = DecisionTreeExplainableModel, classes=['loan_amount','income','age','marital_status'], features=['reject', 'approve'])

Question 4

How can you code an instance of a TabularExplainer for a model named loan_model?

from interpret.ext.blackbox import Explainer tab_explainer = TabularExplainer(loan_model, explainable_model= DecisionTreeExplainableModel, features=['loan_amount','income','age','marital_status'], classes=['reject', 'approve'])

from interpret.ext.blackbox import TabularExplainer tab_explainer = TabularExplainer(model=loan_model, initialization_examples=X_test, features=['loan_amount','income','age','marital_status'], classes=['reject', 'approve'])

from interpret.ext.blackbox import TabularExplainer tab_explainer = TabularExplainer(model=loan_model, initialization_examples=X_test, classes=['loan_amount','income','age','marital_status'], features=['reject', 'approve'])

from interpret.ext.blackbox import TabularExplainer tab_explainer = TabularExplainer(model=loan_model, explainable_model= DecisionTreeExplainableModel, initialization_examples=X_test, features=['loan_amount','income','age','marital_status'], classes=['reject', 'approve'])

Question 5

How can you code a PFIExplainer for a model named loan_model?

from interpret.ext.blackbox import PFIExplainer pfi_explainer = PFIExplainer(model = loan_model, initialization_examples=X_test, classes=['loan_amount','income','age','marital_status'], features=['reject', 'approve'])

from interpret.ext.blackbox import PFIExplainer pfi_explainer = PFIExplainer(model = loan_model, features=['loan_amount','income','age','marital_status'], classes=['reject', 'approve'])

from interpret.ext.blackbox import PFIExplainer pfi_explainer = PFIExplainer(model = loan_model, explainable_model= DecisionTreeExplainableModel, features=['loan_amount','income','age','marital_status'], classes=['reject', 'approve'])

from interpret.ext.blackbox pfi_explainer = PFIExplainer(model = loan_model, initialization_examples=X_test, features=['loan_amount','income','age','marital_status'], classes=['reject', 'approve'])

Question 6

You need to retrieve local feature importance from a TabularExplainer.

How can you code this in the SDK?

local_tab_explanation = tab_explainer.explain_local(X_test[0:5]) local_tab_features = local_tab_explanation.get_ranked_local_names() local_tab_importance = local_tab_explanation.get_ranked_local_values()

local.tab_explanation = tab_explainer_explain_local(X_test[0:5]) local_tab_features = local_tab_explanation.get_ranked_local_names() local_tab_importance = local_tab_explanation.get_ranked_local_values()

local_tab_explanation = tab_explainer.explain_local(X_test[0:5]) local_tab_features = local_tab_explanation.get_feature_local_names() local_tab_importance = local_tab_explanation.get_ranked_local_values()

local_tab_explanation = tab_explainer.explain_local(X_test[0:5]) local_tab_features = local_tab_explanation.get_feature_importance_dict () local_tab_importance = local_tab_explanation.get_ranked_local_values()

Question 7

Which packages do you need to install in the run environment to be able to create an explanation in the experiment script? Select all that apply.