# Machine Learning

There are lists of algorithms which can be utilized for machine learning purpose. However, in below I will divide the list in to two major sections. Section 1 is based upon what I have seen being used in the field (cannot claim that I have seen everything). Section 2 is based on other references.

## Section 1

Regression analysis, Bays and K-Means/kNN are very common  in the practical world, especially in the Government. Sometimes they are utilized as stand alone and in other times, they are used in conjunction with each other or with other models.

1. C4.5 (Decision Trees)
2. k-Means (clustering)
3. k-Nearest Neighbors (kNN)
4. Naive Bayes
5. Regression Analysis (Linear/Multiple/Logistic)
6. Bayesian Networks

Artificial Neural Networks (ANN) might become very popular in near future. I tried to implement this in some of my projects and still doing research on it as part of my PhD research. ANN has unlimited flexibility and prospect and can be utilized for dynamic Machine Learning Environment. However, not many places are using this algorithm as of yet. It is relatively complex to understand and to apply.

## Section 2

In 2006, the IEEE Conference on Data Mining identified the top 10 ML algorithms as

1. C4.5 (Decision Trees)
2. k-Means (clustering)
3. Support Vector Machines (SVM)
4. Apriori
5. Expectation Maximization (EM)
6. PageRank
8. k-Nearest Neighbors (kNN)
9. Naive Bayes
10. Classification and Regression Tree (CART)

An answer to the Quora question, in 2011, lists the following as potential candidates or additions:

1. Kernel Density Estimation and Non-parametric Bayes Classifier
2. K-Means
3. Kernel Principal Components Analysis
4. Linear Regression
5. Neighbors (Nearest, Farthest, Range, k, Classification)
6. Non-Negative Matrix Factorization
7. Support Vector Machines
8. Dimensionality Reduction
9. Fast Singular Value Decomposition
10. Decision Tree
11. Bootstapped SVM
12. Gaussian Processes
13. Logistic Regression
14. Logit Boost
15. Model Tree
16. Naïve Bayes
17. PLS
18. Random Forest
19. Ridge Regression
20. Support Vector Machine
21. Attribute importance: MDL
22. Anomaly detection: one-class SVM
23. Clustering: k-means, orthogonal partitioning
24. Association: A Priori
25. Feature extraction: NNMF

And a 2015 answer provides the following:

1. Linear regression
2. Logistic regression
3. k-means
4. SVMs
5. Random Forests
6. Matrix Factorization/SVD
8. Naive Bayes
9. Artificial Neural Networks
10. For the last one I’d let you pick one of the following:
11. Bayesian Networks
12. Elastic Nets
13. Any other clustering algo besides k-means
14. LDA
15. Conditional Random Fields
16. HDPs or other Bayesian non-parametric model

Fe other algorithms developed or re-developed at the Data Science Central’s research lab:

• Jackknife regression
• Feature extraction / selection (mentioned above, but this version is very different)
• Hidden decision trees
• Indexation and tagging algorithms

[Ref: http://www.datasciencecentral.com/]

3,566 total views, 3 views today