Python Copy # get explanation for the first data point in the test set local_explanation = explainer.explain_local(x_test[0:5]) # sorted feature importance values and feature names sorted_local_importance_names = local_explanation.get_ranked_local_names() sorted_local_importance_values = loca...
This is a core Python principle listed inPEP 20, and it means Django shouldn’t do too much “magic.” Magic shouldn’t happen unless there’s a really good reason for it. Magic is worth using only if it creates a huge convenience unattainable in other ways, and it isn’t implemented ...
Knowing that you can extend it easily gives comfort but there are a ton of features that you get out-of-the-box. Some of them include: Based on a flexible callback system, Catalyst has easily integrated such common Deep Learning best practices, such as gradient accumulatio...
Python Copy # get explanation for the first data point in the test set local_explanation = explainer.explain_local(x_test[0:5]) # sorted feature importance values and feature names sorted_local_importance_names = local_explanation.get_ranked_local_names() sorted_local_importance_values = loc...
Python # get explanation for the first data point in the test setlocal_explanation = explainer.explain_local(x_test[0:5])# sorted feature importance values and feature namessorted_local_importance_names = local_explanation.get_ranked_local_names() sorted_local_importance_values = local_explanation...