Add to ORKGAfter building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted a particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method
Claims about the interpretability of decision trees can be traced back to the origins of machine lea...
Claims about the interpretability of decision trees can be traced back to the origins of machine lea...
Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) m...
After building a classifier with modern tools of machine learning we typically have a black box at h...
Data Mining is the extraction of hidden predictive information from large database. Classification i...
Many machine learning techniques remain ''black boxes'' because, despite their high predictive perfo...
Many machine learning techniques remain ''black boxes'' because, despite their high predictive perfo...
Machine learning is now in a state to get major industrial applications. The most important applicat...
We study the task of explaining machine learning classifiers. We explore a symbolic approach to this...
We study the task of explaining machine learning classifiers. We explore a symbolic approach to this...
Decision tree classifiers have been proved to be among the most interpretable models due to their in...
Decision tree classifiers have been proved to be among the most interpretable models due to their in...
This study describes a model of explanations in natural language for classification decision trees. ...
Decision tree classifiers have been proved to be among the most interpretable models due to their in...
This study describes a model of explanations in natural language for classification decision trees. ...
Claims about the interpretability of decision trees can be traced back to the origins of machine lea...
Claims about the interpretability of decision trees can be traced back to the origins of machine lea...
Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) m...
After building a classifier with modern tools of machine learning we typically have a black box at h...
Data Mining is the extraction of hidden predictive information from large database. Classification i...
Many machine learning techniques remain ''black boxes'' because, despite their high predictive perfo...
Many machine learning techniques remain ''black boxes'' because, despite their high predictive perfo...
Machine learning is now in a state to get major industrial applications. The most important applicat...
We study the task of explaining machine learning classifiers. We explore a symbolic approach to this...
We study the task of explaining machine learning classifiers. We explore a symbolic approach to this...
Decision tree classifiers have been proved to be among the most interpretable models due to their in...
Decision tree classifiers have been proved to be among the most interpretable models due to their in...
This study describes a model of explanations in natural language for classification decision trees. ...
Decision tree classifiers have been proved to be among the most interpretable models due to their in...
This study describes a model of explanations in natural language for classification decision trees. ...
Claims about the interpretability of decision trees can be traced back to the origins of machine lea...
Claims about the interpretability of decision trees can be traced back to the origins of machine lea...
Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) m...