classifying concepts using appropriate type of classification
Answers
Step-by-step explanation:
Organisms can be classified on the basis of several different factors. According to this, the different factors include the nature of the cell; the mode of nutrition seen in organisms and also based on the body organization. Broadly, the following are the different types of classification.
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Answer:Data Mining: Data mining in general terms means mining or digging deep into data which is in different forms to gain patterns, and to gain knowledge on that pattern. In the process of data mining, large data sets are first sorted, then patterns are identified and relationships are established to perform data analysis and solve problems.
Classification: It is a Data analysis task, i.e. the process of finding a model that describes and distinguishes data classes and concepts. Classification is the problem of identifying to which of a set of categories (subpopulations), a new observation belongs to, on the basis of a training set of data containing observations and whose categories membership is known.
Example: Before starting any Project, we need to check it’s feasibility. In this case, a classifier is required to predict class labels such as ‘Safe’ and ‘Risky’ for adopting the Project and to further approve it. It is a two-step process such as :
Learning Step (Training Phase): Construction of Classification Model
Different Algorithms are used to build a classifier by making the model learn using the training set available. The model has to be trained for the prediction of accurate results.
Classification Step: Model used to predict class labels and testing the constructed model on test data and hence estimate the accuracy of the classification rules.
Training and Testing:
Suppose there is a person who is sitting under a fan and the fan starts falling on him, he should get aside in order not to get hurt. So, this is his training part to move away. While Testing if the person sees any heavy object coming towards him or falling on him and moves aside then the system is tested positively and if the person does not move aside then the system is negatively tested.
Same is the case with the data, it should be trained in order to get the accurate and best results.
There are certain data types associated with data mining that actually tells us the format of the file (whether it is in text format or in numerical format).
Attributes – Represents different features of an object. Different types of attributes are:
Binary: Possesses only two values i.e. True or False
Example: Suppose there is a survey of evaluating some product. We need to check whether it’s useful or not. So, the Customer has to answer it in Yes or No.
Product usefulness: Yes / No
Symmetric: Both values are equally important in all aspects
Asymmetric: When both the values may not be important.
Nominal: When more than two outcomes are possible. It is in Alphabet form rather than being in Integer form.
Example: One needs to choose some material but of different colors. So, the color might be Yellow, Green, Black, Red.
Different Colors: Red, Green, Black, Yellow
Ordinal: Values that must have some meaningful order.
Example: Suppose there are grade sheets of few students which might contain different grades as per their performance such as A, B, C, D
Grades: A, B, C, D
Continuous: May have infinite number of values, it is in float type
Example: Measuring weight of few Students in a sequence or orderly manner i.e. 50, 51, 52, 53
Weight: 50, 51, 52, 53
Discrete: Finite number of values.
Example: Marks of a Student in a few subjects: 65, 70, 75, 80, 90
Marks: 65, 70, 75, 80, 90
Syntax:
Mathematical Notation: Classification is based on building a function taking input feature vector “X” and predicting its outcome “Y” (Qualitative response taking values in set C)
Here Classifier (or model) is used which is a Supervised function, can be designed manually based on expert’s knowledge. It has been constructed to predict class labels (Example: Label – “Yes” or “No” for the approval of some event).