Knowledge Base

Knowledge is required to exhibit intelligence. The success of any ES majorly depends upon the collection of highly accurate and precise knowledge.

Factual Knowledge

Factual Knowledge is that knowledge of the task domain that is widely shared typically found in textbook or journals, and commonly agreed upon by those knowledgeable in the particular field.

Heuristic Knowledge

Heuristic Knowledge is the less rigorous, more experiential, more judgemental knowledge of performance. In contrast to factual knowledge, heuristic knowledge is rarely discussed, and is largely individualistic.

Knowledge Representation

Knowledge Representation is the method to organize and formalize the knowledge in the knowledge base. It is the form of IF-THEN-ELSE rules. The IF part of the rule is satisfied; consequently, the THEN part can be concluded.

Knowledge Acquisition

The success of any expert system majorly depends on the quality, completeness, and accuracy of the information stored in the knowledge base. The knowledge base is formed by reading from various expert, scholars, and the knowledge engineers.

Interface Engine

Interface Engine is essential in deducting a correct, flawless solution. It acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution.

User Interface

User Interface provides interaction between user of the ES and the ES itself. It explain how the ES has arrived at a particular recommendation.

Training Set

A set of example used for learning to fit the parameters of the classifier. The training set can be selected by applying a random filter to the data. e.g., select 20% of the points as random to generate the model and test against the remaining 80%.

Validation Set

A set of example used to tune the parameters of a classifier. It is usually used to adjust the classification parameters in order to avoid overfitting.

Test Set

Test Set is the data, whose outcome is already known and is used to determine the accuracy of the machine learning algorithm, based on the training set.

Target Variable

It is the output to be predicted from a machine learning algorithm. It could be binary 0 or 1 if you are classifying or it could be a continuous variable if you are doing a regression.


Classification is the problem of identifying to which of a set of categories a new observation belong, on the basis of a training set of data containing observation whose category membership is known.


Regression analysis is a statistical process for estimating the relationship among variable.


A feature is an individual measurable property of a phenomenon being observed. Features and attributes are individual measurements, when combined with other features make training example.

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