display model prediction interpretation exp explainer explain_instance(norm_corpus[doc_index]lr_pipeline predict_probanum_features= labels=[ ]exp show_in_notebook(the preceding snippet leverages skater to explain our text classifier to analyze its decision-making process in an easy to interpret form even though the model might be complex one in global perspectiveit is easier to explain and approximate the model behavior on local instances this is done by learning the model around the vicinity of the data point of interest by sampling instances around and assigning weightages based on their proximity tox thusthese locally learned linear models help in explaining complex models in more easy to interpret way with class probabilitiescontribution of top features to the class probabilities that aid in the decision making process let' take movie review from our test dataset where both the actual and predicted sentiment is negative and analyze it with the helper function we created in the preceding snippet in [ ]doc_index interpret_classification_model_prediction(doc_index=doc_indexcorpus=norm_test_ reviewscorpus=test_reviewsprediction_labels=test_ sentimentsexplainer_obj=explainertest document index actual sentimentnegative predicted sentiment['negative'reviewworst movie(with the best reviews given iti've ever seen over the top dialogactingand direction more slasher flick than thriller with all the great reviews this movie got ' appalled that it turned out so silly shame on you martin scorsese model prediction probabilities('negative' ('positive' figure - model interpretation for our classification model' correct prediction for negative review