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. Learn More:https://bit.ly/3sX9xuQ Contact Us: Website: https://www.phdassistance.com/ UK: +44 7537144372 India No:+91-9176966446 Email: info@phdassistance.com

1. 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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2. 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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3. 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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