This document discusses key factors to consider when selecting a machine learning algorithm for a problem. It covers the main types of algorithms - supervised, unsupervised, and reinforcement learning. When choosing an algorithm, it is important to understand the data by examining patterns, size, features, and whether the data is input, output, numeric or categorical. The required accuracy and speed also impact the choice, with simpler algorithms being faster but less accurate. Parameters like the number of dimensions and features can increase processing time for some algorithms.

1. An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Phdassistance
Group www.phdassistance.com
Email: info@phdassistance.com

2. TODAY' S
DISCUSSION
Introduction
Understanding the Data
Required Accuracy
Speed
Parameters

3. INTRODUCTION
M achine 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.
T he techniq ues of data categorization
and regression are deemed supervised
learning.
Contd...

4. 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).

5. UNDERSTANDING
T HE DATA
The f i rst and primary stage in determining an a
lg orithm 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.
Contd...

6. 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.

7. Figure 1:Types of Machine Learning Algorithms

8. The feature of
parameter. The
data is another
way the data is
created, and whether it is linear to
the data must be considered.
Then maybe a linear model is most
such as regressions or
However, if is
suited,
SVM.
more complicated
your data
then
complicated algorithms
more
like
Random forest may be required.
Contd...

9. 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 i f the input data
are labeled; otherwise, unsupervised
algorithm must be used. If
numerical, on the other hand,
the output is
then regression
will be used, but if it is a collection of groups, it
is an issue of clustering .
Contd...

10. In the next step, i t 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
observation near to the
from a given
right response
(Garg, 2020 ).
Contd...
REQUIRED ACCURACY

11. Sometimes a correct reply to our
target application is not essential.
by adopting
If the approximation is strong
an
may
enough,
approximate
considerably
model, we
reduce the training
and processing time.
Approximation approaches, such as
linear regression
data, prevent or
of non- linear
do not execute
data overfitting.

12. S P E E D
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 t imeline, while
faster processing has less accuracy.
The incredibly simple algorithms l ike
Naïve Bayes and Logistic regression are
used often since they' re simple, quick to
run algorithms.
C o n td ...

13. Using more advanced techniques l ike support vector machine l
earning, 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 t ime
more important or the accuracy .
I f i t is t ime, simpler methods must be used, while i f accuracy is
more important, then one has to go with more sophisticated ones.

14. P A R A M E T E R S
The parameters will impact how the
algorithm behaves . Options that alter the
algorithm' s behavior, such as tolerance
for error or the number of i terations.
For as many parameters as the data has, t
ime required to process the data t raining
and processing t ime is f requently
proportional.
C o n td ...

15. the number
The greater
dimensions ,
However, an
the more
algorithm
time
with
of parameters the
it takes to process
numerous parameters
model' s
and t rain.
means the
method is adaptable.
Machine learning addresses measurable variables. Having more
features might slow down certain algorithms, therefore this
causes them to take a lengthy t ime 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.

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