How do you write a case based discussion?

How do you write a case based discussion?

1. Ask the trainee to introduce the case briefly – try not to interrupt (2-3 mins).
2. Clarify matters of fact e.g. ‘what did you mean when you told her….
3. Take notes as they talk: especially of some of the things they say that relate to the competency domains you want to assess; explore those later.

What is case based teaching?

Case-based teaching is a pedagogical approach that engages students in the process of making real-world decisions. You create cases that represent authentic workplace situations to encourage students to apply knowledge gained from the classroom or through additional research in order to solve the case.

What is an example of case based learning?

Using a case-based approach engages students in discussion of specific scenarios that resemble or typically are real-world examples. This method is learner-centered with intense interaction between participants as they build their knowledge and work together as a group to examine the case.

Which are the following applications can be solved using Case Based Reasoning?

Applications of CBR includes: Problem resolution for customer service help desks, where cases describe product-related diagnostic problems. It is also applied to areas such as engineering and law, where cases are either technical designs or legal rulings, respectively.

What is Case Based Reasoning in ML?

Case-based reasoning, broadly construed, is the process of solving new problems based on the solutions of similar past problems. #

What is the primary reason of Cadet Case Based Reasoning?

The area of AI concerned with case-based reasoning puts Schank’s memory-based reasoning model in practice. In a nutshell, CBR is reasoning by remembering: previously solved problems (cases) are used to suggest solutions for novel but similar problems.

Which of the following does CBR relate to?

The California Bearing Ratio (CBR) test is a penetration test used to evaluate the subgrade strength of roads and pavements. The results of these tests are used with the curves to determine the thickness of pavement and its component layers. This is the most widely used method for the design of flexible pavement.

What is rule based reasoning?

A Rule Based System (RBS) consists of a knowledge base and an inference engine. In addition to its static memory for facts and rules, an RBS uses a working-memory to store temporary assertions. The other types of reasoning systems are: Case based system (CBS) and Model based systems (MBS).

What is the rule-based approach?

In computer science, a rule-based system is used to store and manipulate knowledge to interpret information in a useful way. It is often used in artificial intelligence applications and research. Normally, the term rule-based system is applied to systems involving human-crafted or curated rule sets.

What is a rule-based process?

1. A process which applies to familiar situations and is governed by the application of a set of explicit rules or heuristics ( Rasmussen, 1983 ). Learn more in: The Aftermath of HIPAA Violations and the Costs on U.S. Healthcare Organizations. Rule-Based Process appears in: Encyclopedia of Information Science and…

What is a conflict set of rules?

The rules which could fire at any moment in time are known as the conflict set. A Conflict Resolution Strategy is required to make the decision as to which rule should be fired first.

What is a system rule?

1. system of rules – a complex of methods or rules governing behavior; “they have to operate under a system they oppose”; “that language has a complex system for indicating gender” system. method – a way of doing something, especially a systematic way; implies an orderly logical arrangement (usually in steps)

What are the main components of a rule-based system?

A general rule-based expert system consists of six components: knowledge base, knowledge acquisition facility, database, inference engine, explanation facility and user interface. A functional integration of these components is shown in Fig. 2.1. The functions of these components are described on the next page.

What are the types of expert system?

There are mainly five types of expert systems. They are rule based expert system, frame based expert system, fuzzy expert system, neural expert system and neuro-fuzzy expert system. We discussed the expert systems based on their knowledge representation, inference engine, working of the system and user interface.

What is rule based filtering?

Rule-based filtering is a technique that enables to limit precisely a flow of documents to those that deal with particular topics and to highlight parts of texts that have been used to select the documents. This technique requires an administrator to develop a set of rules for each topic.

What is rule based classifier?

The term rule-based classification can be used to refer to any classification scheme that make use of IF-THEN rules for class prediction. They are also used in the class prediction algorithm to give a ranking to the rules which will be then be utilized to predict the class of new cases.

How does a rule based classifier work?

Here we will learn how to build a rule-based classifier by extracting IF-THEN rules from a decision tree. One rule is created for each path from the root to the leaf node. To form a rule antecedent, each splitting criterion is logically ANDed. The leaf node holds the class prediction, forming the rule consequent.

How will you counter Overfitting in the decision tree?

increased test set error. There are several approaches to avoiding overfitting in building decision trees. Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set. Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree.

Is decision tree rule based?

Rule based decision tree (RBDT) Most of the methods that generate decision trees for a specific problem use examples of data instances in the decision tree generation process. RBDT-1 method uses a set of declarative rules as an input for generating a decision tree.

Which library is used to build the decision tree model?

Coding a decision tree The scikit-learn dataset library already has the iris dataset. You can either use the dataset from the source or import it from the scikit-learn dataset library. There are 150 examples/ samples in the data. The variable ‘X’ contains the attributes to the iris plant.

What is unordered rule list?

To produce such a list, rules are learned for each class in turn, starting with the smallest (in terms of relative frequency of occurrence) and ending with the second largest one. To avoid these problems, FURIA learns an unordered set of rules, namely a set of rules for each class in a one-vs-rest scheme.

Which one of the following is the indirect method to extract the rules?

Examples: RIPPER, CN2, Holte’s 1R Indirect Method: Extract rules from other classification models (e.g. decision trees, neural networks, etc).

What are two steps of tree pruning work?

The tree is pruned back to the point where the cross-validated error is a minimum. Cross-validation is the process of building a tree with most of the data and then using the remaining part of the data to test the accuracy of the decision tree.

What is true rule-based learning?

Rule-based machine learning approaches include learning classifier systems, association rule learning, artificial immune systems, and any other method that relies on a set of rules, each covering contextual knowledge.

What is Ripper algorithm?

The Ripper Algorithm is a Rule-based classification algorithm. It derives a set of rules from the training set. It is a widely used rule induction algorithm. Uses of Ripper Algorithm: It works well on datasets with imbalanced class distributions.

What is sequential covering algorithm?

2 Sequential Covering. Sequential covering is a general procedure that repeatedly learns a single rule to create a decision list (or set) that covers the entire dataset rule by rule. Many rule-learning algorithms are variants of the sequential covering algorithm.

What is the difference between rule-based and learning based approach?

Rule-based systems rely on explicitly stated and static models of a domain. Learning systems create their own models.