Summarizes the algorithms and provides the best road-map

Assignment Help Data Structure & Algorithms

Reference no: EM131470237

Review the classification algorithms provided below and compile a thorough summary and comparison of the algorithms. Summary should serve as a resource for algorithm selection and should include items like:

type of classification: binary, multiclass
type of model (whichever applies): linear, nonlinear, probabilistic, generative, discriminative, eager, lazy, etc.
high bias, high variance?
main application domains
general characteristics
advantages
disadvantages
training/classification speed
how much parameter tuning needed
for description, prediction, or both (interpretability)
what attribute types can be handles; mixed?
type of data preprocessing needed (e.g., data type, scaling, transformation)
ability to handle noise, irrelevant features
ability to handle high-dimensional data
things to watch out for

and so on. I expect that you will reach outside the class resources to find more information; however, please any resource used should be referenced appropriately.

You are free to format your summary according to what you think best summarizes the algorithms and provides the best road-map for algorithm selection - it may be a list, a table, a mind-map, or a road-map (e.g., for example scikit- learn algorithm cheat-sheet or Microsoft Azure ML cheatsheet).

Classifications & Algorithms:

Decision Trees (ID3, CART, CHAID)
Nearest Neighbor (K-NN)
Bayesian Classifiers
Artificial Neural Networks
Support Vector Machines
Ensemble Methods
Class Imbalance and Multiclass Problems

Reference no: EM131470237

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len1470237

4/22/2017 3:48:47 AM

Please review attached document. Provide the solution in MS word format and reference any source used. You are free to format your summary according to what you think best summarizes the algorithms and provides the best road-map for algorithm selection – it may be a list, a table, a mind-map, or a road-map (e.g., for example scikit- learn algorithm cheat-sheet or Microsoft Azure ML cheatsheet). Review the classification algorithms provided below and compile a thorough summary and comparison of the algorithms.

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