Motivation &
Introduction:
Machine
learning aim to develop computer programs that can access data and learn
themselves. This automation without human interaction or intervention if
mimicked similar to the structures of
brain then such programs or applications becomes an Artificial Intelligent
systems. If they are as such used for constructive purpose to support
technological advancements it is best way forward for human’s survival of fittest
[1].
Methodology:
Questions
like “Which algorithm to be used?”, “What are the types & techniques in
Machine Learning algorithms?” forms the main hypothesis to be answered to
through this survey[2].
Literature:
The main ideas to think for a problem solver with machine learning
algorithms were: What is the Business problem, Is my Objective requirements Operational/Strategic,
How will a model be implemented & Algorithms capability that matches with
requirements. The
3 ways of possibility to define the requirements are: Traditional statistical
techniques on fundamentals, self-learning is ML & Neural networks are deep
learning.
Survey
on Real-world application and Research Directions from cybersecurity, smart
cities, healthcare, e-commerce, agriculture etc.,[3] is the actual
motivation for anyone to start with selection and dissemination of such knowledge
structures in the machine learning arena. This should start from fundamentally
dealing with the hypothesis discussed here. How
to Select the Right Machine Learning Algorithm [4] describes the 7
key factors to consider when you have to select the right machine learning
algorithms. The basis again where to use which algorithm is dealt with in click
[5].
Guide to choose the right machine learning algorithm
[6]:
- Size of the training data
- Accuracy and/or interpretability of the output
- Speed or Training time
- Linearity
- No. of features
Figure
courtesy: Choose-ml-technique at serokell.io[7]
Essentials,
Principles, Types & Examples [8]:
- Data Vs Algorithm – terms to understand
- Explainability vs Accuracy trade-offs
- Principle is Y=f(X) in supervised learning
- Types or Techniques:
o Supervised
o Un-Supervised
o Semi-Supervised
o Reinforcement
·
Examples: Based on Space, time and Output
·
How to run ML Algorithms
Figure courtesy Hacker noon [9].
Commonly used ML
algorithms in 2021[10]:
·
Supervised
o Linear
regression
o Logistic
regression (a subset of Neural network)
o Decision
tree, CART
o SVM
algorithm
o Naive
Bayes algorithm
o KNN
algorithm
·
Unsupervised:
o K-means
o Random
forest algorithm
o Dimensionality
reduction algorithms, PCA
o Association,
Apriori, ANN [11]
·
Ensembling:
o Ensembling
(Bagging, Bootstrap sampling) [12]
o Gradient
boosting algorithm and AdaBoosting algorithm
·
Reinforcement, Q-learning, Model-based value
estimations,
o GAN,
Self-trained Naive Bayes [13]
Figure courtesy: Hui Li,
ML Cheat Sheet [15]
· Further tour:
ML Algorithms goes into more detailed single
algorithms under each time[14]. Good works suggests trends
in similar lines for all portfolios of technology and the specific one on ML
gives similar overview as all above at [17]. Scikit-learn[18]
cheat sheet is quiet eye-opener. Compare
two or more algorithms as suggested with step by step algorithms selection with
python code at [16].Good works suggests trends in similar lines for
all portfolios of technology and the specific one on ML gives similar overview
as all above at [17]. Scikit-learn[18]
cheat sheet is quiet an eye-opener.
Figure Courtesy Scikit-learn[18] Hacker noon
Case : for
Azure ML[20]
The use case steps suggested for setup of a business requirement from Microsoft Azure found to be practical with the available resources and parameter tuning in own cloud setup
1.
Business
scenarios and the Machine Learning Algorithm Cheat Sheet
2. Comparison of
machine learning algorithms
3. Requirements for
a data science scenario
4. Accuracy
5. Training time
6. Linearity
7. Number of
parameters
8. Number of
features
Case for Smarter cities:
The
Digital Twin Paradigm for Smarter Systems and Environments: The Industry use
cases , many of the above and Canonical correlation Analysis for feature
extraction type of task [20].
Case with Experiment implementation:
Commonly used Machine
Learning Algorithms (with Python and R Codes) [22].
As final remarks of a job interview questions and the like on how to choose consists of first classify the decision into data related and problem related [21].
Conclusion:
The survey put forward the
ideas around machine learning by answering the hypotheses on which algorithms
to be picked when and what does each one do. Though the writing itself is not
detailed enough, it is a good collection for answering the fundamental
questions such that the reader gets an overview in the field of Machine
learning.
References:
[1] https://www.expert.ai/blog/machine-learning-definition/
[2] https://www.analytixlabs.co.in/blog/how-to-choose-the-best-algorithm-for-your-applied-ai-ml-solution/
[3] Sarker, I.H.
Machine Learning: Algorithms, Real-World Applications and Research
Directions. SN COMPUT. SCI. 2, 160 (2021).
https://doi.org/10.1007/s42979-021-00592-x
[4] https://towardsdatascience.com/how-to-select-the-right-machine-learning-algorithm-b907a3460e6f
[5] https://www.datacamp.com/community/news/which-machine-learning-algorithm-should-i-use-raluply88ph
[6] https://www.kdnuggets.com/2020/05/guide-choose-right-machine-learning-algorithm.html
[7] https://serokell.io/blog/how-to-choose-ml-technique
[8] https://www.knowledgehut.com/blog/data-science/machine-learning-algorithms
[9] https://hackernoon.com/choosing-the-right-machine-learning-algorithm-68126944ce1f
[10] https://www.simplilearn.com/10-algorithms-machine-learning-engineers-need-to-know-article
[11]
https://www.dezyre.com/article/common-machine-learning-algorithms-for-beginners/202/
[12] https://www.dataquest.io/blog/top-10-machine-learning-algorithms-for-beginners/
[13] https://searchenterpriseai.techtarget.com/feature/5-types-of-machine-learning-algorithms-you-should-know
[14] https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
[15] https://blogs.sas.com/content/subconsciousmusings/2020/12/09/machine-learning-algorithm-use
[16]https://analyticsindiamag.com/how-to-choose-the-best-machine-learning-algorithm-for-a-particular-problem
[17] https://www.goodworklabs.com/machine-learning-algorithm
[18] https://hackernoon.com/hn-images/1*9gGtNn2EXW1Zog-bjdGsHQ.png
[19] https://docs.microsoft.com/en-us/azure/machine-learning/how-to-select-algorithms
[20] https://www.sciencedirect.com/topics/engineering/machine-learning-algorithm
[21] https://www.youtube.com/watch?v=gZWhVj-g8Mc
[22] https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms
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