How the Nearest Neighbor and Naïve Bayes Work Discussion

Peer responses must be substantive in nature.Build on something your classmate said.Explain why and how you see things differently.Ask a probing or clarifying question.Share an insight from having read your classmate’s posting.Offer and support an opinion. Ask a probing or clarifying question. Discussion 1:
1. Several classifiers are used in data mining to classify data into predefined
classes and categories. Some essential classifiers are Decision Tree Classifiers,
Naïve Bayes Classifier, Neural Network Classifier, Rule-Based Classifier, Support
Vendor Machines, etc. Each classifier has its benefits and drawbacks in data
mining, and selecting the appropriate classifier is significant.
2. In the rule-based classifier, each rule consists of a condition corresponding to a
class label. To classify data instances, a rule-based classifier uses an if-then
condition and offers flexibility to determine each data set in the classifier rule.
Depending on the possible requirements of the problem, it is vital to use a rulebased classifier. Rules are typically derived from predefined categories and
required sequential rules.
3. Nearest Neighbor and Naïve Bayes classifiers are popular machine learning
(ML) algorithms but differ in principles and assumptions. The nearest Neighbor
determines the class of a new data point, and Naïve Bayes calculates the
probability for each class given in the data point features. Handling missing values
and interpretability is also different in the two classifiers and requires a trade-off to
work on computational accuracy (Meller et al., 2018).
4. Logistic regression is a popular algorithm used in analyzing large data sets and
estimating the probability of an event. Logistic regression is a widely used
application in data mining to make informed decisions and simplify complexities
in managing the data. It is significant to work on logistic regression to uncover the
relationship between independent variables and a binary outcome ( Shu & Ye,
2023).
References
Meller, J., Stone, M., & Keane, J. (2018). Applications of Data Mining to Big
Data: Principles and Potentials. Journal of Indexing and Metrics.
Shu, X., & Ye, Y. (2023). Knowledge Discovery: Methods from data mining and
machine learning. Social Science Research, 110, 102817.
Discussion 2:
Questions 1
A classifier is a data mining algorithm that categorizes input data. Various
classifiers include perceptron, naïve Bayes, decision tree, logistic regression, knearest neighbor artificial neural networks, and support vector machine. Logistic
regression is important to understand the influence of various variables on a
particular outcome variable. It is meant for classification purposes.
Naïve Bayes uses the Bayes theorem for spam filtering and document
classification. It needs a little training of data for parameters estimation. K nearest
neighbor classifier does not construct models but can store training data instances.
The support vector machine classifier represents training data points as clear gaps
for space separation. It helps store the memory of training data. However, it cannot
issue probability estimates. A decision tree provides a sequence of rules that is
useful in classifying data.
Question 2
A rule-based classifier is a type of classifier that is helpful for class decisions
(Berhane et al., 2018). It uses various rules such as if or else. The rule-based
classifier uses simple rules for interpretation, therefore, this classifier helps
generate descriptive models. The ‘if’ term is known as an antecedent.
Question 3
The main difference between k-nearest neighbor and naïve Bayes is the large
real-time computation required by the KNN than naïve Bayes. Naïve Bayes
classier is very fast compared to k-nearest neighbor due to the real-time execution.
K-nearest neighbor is a non-parametric classifier while naïve Bayes is a parametric
classifier. Another difference is that naïve Bayes is a linear classifier while knearest neighbor is a not. While applying large amounts of data, KNN is quite slow
than naïve Bayes.
Question 4
Logistic regression is a classification algorithm that is useful for predicting
binary results according to a set of independent variables (López-Martínez et al.,
2018). Logistic regression is a predictive analysis that can describe data and
explains the correlation of binary variables to nominal or ordinal variables. It can
estimate the log odds of an event.
References
López-Martínez, F., Schwarcz, A., Núñez-Valdez, E. R., & Garcia-Diaz, V.
(2018). Machine learning classification analysis for a hypertensive
population as a function of several risk factors. Expert Systems with
Applications, 110, 206-215.
Berhane, T. M., Lane, C. R., Wu, Q., Autrey, B. C., Anenkhonov, O. A.,
Chepinoga, V. V., & Liu, H. (2018). Decision-tree, rule-based, and random
forest classification of high-resolution multispectral imagery for wetland
mapping and inventory. Remote sensing, 10(4), 580.