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Desicion Tree, Clustering, Evaluation Method, How to use it?, What is it?,…
Desicion Tree
- Input relevant variables with their respective probability values.
- Determine and allocate payoffs for each possible outcome.
- Draw a decision tree with all possible solutions and their consequences.
- Calculate the Expected Monetary Value for every chance node in order to determine which solution is expected to provide the most value. Circles represent chance nodes in a tree diagram.
- Define the problem area for which decision making is necessary.
A decision tree is a structure that includes a root node, branches, and leaf nodes.
Each branch denotes the outcome of a test meanwhile, each internal node denotes a test on an attribute, and each leaf node holds a class label. The topmost node in the tree is the root node.
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Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning.
Clustering
- One group means a cluster of data.
- Data sets are divided into different groups in the cluster analysis, which is based on the similarity of the data.
- In clustering, a group of different data objects is classified as similar objects.
- After the classification of data into various groups, a label is assigned to the group.
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Clustering analysis is broadly used in many applications such as market research, pattern recognition, data analysis, and image processing.
Clustering can also help marketers discover distinct groups in their customer base. And they can characterize their customer groups based on the purchasing patterns.
Clustering is the grouping of specific objects based on their characteristics and their similarities.
an unsupervised Machine Learning-based Algorithm that comprises a group of data points into clusters so that the objects belong to the same group.
Clustering is an unsupervised machine learning method of identifying and grouping similar data points in larger datasets without concern for the specific outcome.
Clustering (sometimes called cluster analysis) is usually used to classify data into structures that are more easily understood and manipulated.
Evaluation Method
The process of constructing and using the data warehouse. A data warehouse is constructed by integrating the data from multiple heterogeneous sources. It supports analytical reporting, structured and/or ad hoc queries, and decision making.
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After the data mining algorithm has concluded, the result must be evaluated
How to use it?
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- Holdout method and cross validation
- Leave-one-out Cross Validation
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