Selecting the Right Type of Algorithm for

Various Applications

Dr. Nancy Agnes, Head, Technical Operations Phdassistance, info@phdassistance.co

Keyword

PhdAssistance / Insights / Computer Science

/ Machine Learning / Selecting the right type

of algorithm for various applications

I. INTRODUCTION

Machine learning algorithms may be classified

mainly into three main types. Supervised

learning constructs a mathematical model

from the training data, including input and

output labels. The techniques of data

categorization and regression are deemed

supervised learning. In unsupervised learning,

the system constructs a model using just the

input characteristics but no output labeling.

The classifiers are then trained to search the

dataset for a specific pattern. Examples of

uncontrolled learning algorithms including

clustering and segmentation. In reinforcement

learning, the model learns to complete a task

in reinforcement learning by executing a

number of actions and choices that it improves

itself and then understands from the

information from these actions and decisions

(Lee & Shin, 2020).

Figure 1: Types of Machine Learning Algorithms

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II. UNDERSTANDING THE DATA

The first and primary stage in determining an

algorithm is the understanding of your data.

One needs to acquaint themselves with data

before thinking about the various algorithms.

One easy approach of doing this is to view

data and attempt to detect patterns in them, to

watch their behavior and especially their size.

The size of the data is an important parameter.

Some algorithms do better than others with

greater data (Mahfouz et al., 2020). For

instance, algorithms with higher bias or lower

variance classification are more effective than

lower bias or higher variance classifications in

limited training datasets (Richter et al., 2020).

For instance, Naïve Bayes will do better than

kNN if the training data is smaller.

The feature of data is another parameter. The

way the data is created, and whether it is

linear to the data must be considered. Then

maybe a linear model is most suited, such as

regressions or SVM. However, if your data is

more complicated then more complicated

algorithms like Random forest may be

required. The features being linked or

sequential also requires specific type of

algorithms. The type of data is an important

parameter (Vabalas et al., 2019). The data

maybe classified into input or output. Use a

supervised learning method if the input data

are labeled; otherwise, unsupervised algorithm

must be used. If the output is numerical, on

the other hand, then regression will be used,

but if it is a collection of groups, it is an issue

of clustering.

III. REQUIRED ACCURACY

In the next step, it should be decided whether

or not accuracy is important for the issue one

is attempting to address. The accuracy of an

application refers to the capacity of an

individual method to estimate a response from

a given observation near to the right response

(Garg, 2020). Sometimes a correct reply to our

target application is not essential. If the

approximation is strong enough, by adopting

an approximate model, we may considerably

reduce the training and processing time.

Approximation approaches, such as linear

regression of non-linear data, prevent or do

not execute data overfitting.

IV. SPEED

Sometimes users have to choose between

speed and accuracy in order to decide on an

algorithm. Typically, more precision takes

longer to achieve, over a longer timeline,

while faster processing has less accuracy. The

incredibly simple algorithms like Naïve Bayes

and Logistic regression are used often since

they're simple, quick to run algorithms. Using

more advanced techniques like support vector

machine learning, neural networks, and

random forests, might take a lot longer to

learn, and would also give higher accuracy.

Therefore, the question is how much is the

project worth, Is time more important or the

accuracy. If it is time, simpler methods must

be used, while if accuracy is more important,

then one has to go with more sophisticated

ones.

V. PARAMETERS

The parameters will impact how the algorithm

behaves. Options that alter the algorithm's

behavior, such as tolerance for error or the

number of iterations. For as many parameters

as the data has, time required to process the

data training and processing time is frequently

proportional. The greater the number of

parameters the model's dimensions, the more

time it takes to process and train. However, an

algorithm with numerous parameters means

the method is adaptable. Machine learning

addresses measurable variables. Having more

features might slow down certain algorithms,

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therefore this causes them to take a lengthy

time to train. So long as the issue has a large

feature set, one should choose an algorithm

such as SVM, which is best suited to those

with numerous features.

REFERENCES

Garg, A. (2020). Comparing Machine

Learning Algorithms and Feature Selection

Techniques to Predict Undesired Behavior in

Business Processesand Study of Auto ML

Frameworks. https://www.diva-

portal.org/smash/record.jsf?pid=diva2%3A14

98973&dswid=-4298

Lee, I., & Shin, Y. J. (2020). Machine learning

for enterprises: Applications, algorithm

selection, and challenges. Business Horizons,

63(2), 157–170.

https://doi.org/10.1016/j.bushor.2019.10.005

Mahfouz, A. M., Venugopal, D., & Shiva, S.

G. (2020). Comparative Analysis of ML

Classifiers for Network Intrusion Detection

(pp. 193–207). https://doi.org/10.1007/978-

981-32-9343-4_16

Richter, C., Hüllermeier, E., Jakobs, M.-C., &

Wehrheim, H. (2020). Algorithm selection for

software validation based on graph kernels.

Automated Software Engineering, 27(1–2),

153–186. https://doi.org/10.1007/s10515-020-

00270-x

Vabalas, A., Gowen, E., Poliakoff, E., &

Casson, A. J. (2019). Machine learning

algorithm validation with a limited sample

size. PLOS ONE, 14(11), e0224365.

https://doi.org/10.1371/journal.pone.0224365

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