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Decision tree, Evaluation method, Clustering - Coggle Diagram
Decision tree
When to use it?
Decision trees are a great way to deal with non-linear data, which always involve a lot of variables and are usually full of uncertainty.
What is it?
A decision tree is a type of probability tree (flow chart) that allows you to make a decision about a specific process.
How to use it?
Identify the main objective, and put it on to of decision tree.
Draw arrow lines for every possible course of action, stemming from the root.
Attach leaf nodes at the end of your branches.This makes the process easier for the staff to understand and follow.
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Where to use it?
Decision trees are used to solve a variety of problems in a variety of industries. They're used in a variety of industries, from technology to health care to financial planning, due to their adaptability.
For Example, banks and mortgage companies use historical data to estimate the probability of a borrower defaulting on their payments.
For example, a shoe company deciding where to target its limited advertising budget, based on what demographic data suggests customers are likely to buy.
Evaluation method
How to use it?
Hold Out method is for training a machine learning model the process of splitting the data in different splits and using one split for training the model and other splits for validating and testing the models
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Random Sub-sampling is method where hold out method will be repeated several times to improve the estimation of a classifier performance.
k-fold Cross-validation is method where the data set will be divided into k subsets, and the holdout method will be repeated k times. The advantage of doing this is that you can independently choose how large each set is and how many trials you average over.
Bootstrap is method that lets you generate many sample datasets by repeatedly sampling from your existing datad
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Where to use it?
Evaluation method are used when evaluating model performance for the data used and how well the chosen model will work.
What is it?
Evaluation method is integral part of the model development process. It helps to find the best model that represents our data and how well the chosen model will work in the future
Clustering
How to use it?
Model-Based Method
Every cluster is hypothesized so that it can find the data which is best suited for the model which is to locate the group.
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Grid-Based Method
The objects are used to create a grid. The object space is quantified into a fixed amount of cells to create a Grid Structure.
Constraint-Based Method
Application or user-oriented constraints are incorporated to perform the clustering. The expectation of the user is referred to as the constraint.
Partitioning Method
A cluster will be represented by each partition and m < p. K is the number of groups after the classification of objects
Density-Based Method
The cluster will keep on growing continuously. At least one number of points should be there in the radius of the group for each point of data.
When to use it?
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Starting from a large, unstructured dataset
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Where to use it?
Helps marketers to find the distinct groups in customer base and characterize their customer groups by using purchasing patterns.
In image processing, data analysis, and pattern recognition.
In the field of biology, by deriving animal and plant taxonomies, identifying genes with the same capabilities.
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What is it?
Clustering used to find out the group of objects which are similar to each other in the group but are different from the object in other groups.