Your All-in-One AI-Powered Toolkit for Academic Success.

Available 24*7 on WhatsApp / Email

Unlock your academic potential

ยฉ 2024 ย | ย Zucol Services PVT LTD ย | ย All rights reserved.

Added on ย 2023/06/05

|9

|1750

|265

AI Summary

This article discusses the different data mining techniques such as decision tree, naive bayes, and k-nearest neighbor algorithms. It explains the basic assumptions when creating decision trees and the concept of confusion matrix in summarizing the performance of classification algorithms. The article also provides insights on the difference in performance between the algorithms.

Your contribution can guide someoneโs learning journey. Share your
documents today.

Data mining.

Name.

Institutional affiliation.

Name.

Institutional affiliation.

Need help grading? Try our AI Grader for instant feedback on your assignments.

Calcification can be performed either in a structured or unstructured way. For this part of the

analysis, we used classification which is the techniques where we do a recognition of a given

case of algorithms. Classification aims at identifying the new data and how this new data will fall

into the research objectives This classification will look at three major factors that can be used to

identify the data mining techniques

Decision Tree

The main aim of the decision tree is actually to create a model that can be used in predicting the

class or even the value by learning the rules and data management. Understanding decision tree

algorism is not very hard but it comparison is somehow complicated. A decision tree is where

each and every node represents its feature. From the analysis, each link while each outcomes. In

This analysis the main thing is to create a decision tree that is similar to the entire process.

Understanding decision tree is very easy when it is compared with the classification algorithms.

It tries to solve every problem using a tree replacement representation (Ting, 2017). Each and

every internal nodes corresponds to an attribute and each and every leaf will correspond to a

class label. When using a decision to predict the label for a god record, the process starts with the

roots of the tree. We do a comparison of the root t attribute. Based on this comparison, we must

follows the corresponding to that value and jumps to the next value. The record comparison

continues using an internal record tree until the entire process reach the leaf node. Knowing how

the modelled decision can work, in predicting the target class or the value now the understating

of how to create decision tree model is brought forward.

analysis, we used classification which is the techniques where we do a recognition of a given

case of algorithms. Classification aims at identifying the new data and how this new data will fall

into the research objectives This classification will look at three major factors that can be used to

identify the data mining techniques

Decision Tree

The main aim of the decision tree is actually to create a model that can be used in predicting the

class or even the value by learning the rules and data management. Understanding decision tree

algorism is not very hard but it comparison is somehow complicated. A decision tree is where

each and every node represents its feature. From the analysis, each link while each outcomes. In

This analysis the main thing is to create a decision tree that is similar to the entire process.

Understanding decision tree is very easy when it is compared with the classification algorithms.

It tries to solve every problem using a tree replacement representation (Ting, 2017). Each and

every internal nodes corresponds to an attribute and each and every leaf will correspond to a

class label. When using a decision to predict the label for a god record, the process starts with the

roots of the tree. We do a comparison of the root t attribute. Based on this comparison, we must

follows the corresponding to that value and jumps to the next value. The record comparison

continues using an internal record tree until the entire process reach the leaf node. Knowing how

the modelled decision can work, in predicting the target class or the value now the understating

of how to create decision tree model is brought forward.

Basic assumption when creating decision tree.

When we create decision tree, we assume that the entire process considered a roots.

Features values are actually preferred to be categorical. In case the values continues then they are

considered to be discrete and building model for the entire tree.

The record used are distributed recursively based on the attributes of the entire value. The order

of placing the attribute in the internal node by using same statistical approach. Research shows

that decision tree follows the concept of sum of product representation. The images formed on

weka shows how the values can be compatible. This description is also known as a descriptive or

disjunction or normal form. For every class, every root of the tree pose the same class

Naive Bayes

To demonstrate this concept, we must consider an example displays in an illustration based on

procedure and experimental factors. Based on the indication, the objective is either a green or the

intakes is to put a classification based on the data that arrived first. It also define which level the

decision tree is at the time of analysis. When two variables appear such as twice green, then we

conclude that there is a reasonable membership and case study. The baiyan analysis recognize

this as prior profitability. Prior profitability are actually based on the previous experience. In this

case we have a better number of information compared to the actually data and the number of

repeated happenings.

To get the exact information according to the data required, we must formulate a probability.

After doing this, we must perform a classification. This is done easily because the objects are

When we create decision tree, we assume that the entire process considered a roots.

Features values are actually preferred to be categorical. In case the values continues then they are

considered to be discrete and building model for the entire tree.

The record used are distributed recursively based on the attributes of the entire value. The order

of placing the attribute in the internal node by using same statistical approach. Research shows

that decision tree follows the concept of sum of product representation. The images formed on

weka shows how the values can be compatible. This description is also known as a descriptive or

disjunction or normal form. For every class, every root of the tree pose the same class

Naive Bayes

To demonstrate this concept, we must consider an example displays in an illustration based on

procedure and experimental factors. Based on the indication, the objective is either a green or the

intakes is to put a classification based on the data that arrived first. It also define which level the

decision tree is at the time of analysis. When two variables appear such as twice green, then we

conclude that there is a reasonable membership and case study. The baiyan analysis recognize

this as prior profitability. Prior profitability are actually based on the previous experience. In this

case we have a better number of information compared to the actually data and the number of

repeated happenings.

To get the exact information according to the data required, we must formulate a probability.

After doing this, we must perform a classification. This is done easily because the objects are

well clustered. This makes it known that there are more data and object vicinity compared to the

new cases that belonged to the color. To have a real measure of likelihood we must draw a circle

around X which is a combination of numbers and irrespective of their own classes and labels.

After this, we have to do a detailed calculated.

Although we have an assumption that the independent and dependent variable are not always

very accurate, this process does simplify the dramatically since the class conditions and densities

are calculated separately for every variable. Byuyan performs a reduction of density estimation

and task to a one dimetnila kernel density estimations. Moreover, this assumption does not

necessarily give a different probability of certain boundaries.

K-nearest neighbor algorithms.

This is anon parametric method that I used t clarify and perform a regression analysis. In both

cases, the input considered the k-the closet training in the feature space. In simple terms, the

structure of this is basically determent and it is proving very useful in the real world. This is not

basically meant to obey te assumptions produced by some of the other algorithms. In this regard,

this algorithms can be one of the choices used when there is a serious distribution of data. This

algorithms is also called lazy algorithms because according to the analysis, it side not use the

training data and does not point to many particular generalization. There is not explicit training

or if there is any it is very minimal. The output seen in this case is a class based membership

which means that is can be predicted. In this algorithm, the objects is basically classified a

majority of votes complied with the neighbors and is assigned to class of the most common

among the group. It is also used to regression. In most cases, the output is value of the object in

question and due to this, the average value is received from the K nearest neighbor.

new cases that belonged to the color. To have a real measure of likelihood we must draw a circle

around X which is a combination of numbers and irrespective of their own classes and labels.

After this, we have to do a detailed calculated.

Although we have an assumption that the independent and dependent variable are not always

very accurate, this process does simplify the dramatically since the class conditions and densities

are calculated separately for every variable. Byuyan performs a reduction of density estimation

and task to a one dimetnila kernel density estimations. Moreover, this assumption does not

necessarily give a different probability of certain boundaries.

K-nearest neighbor algorithms.

This is anon parametric method that I used t clarify and perform a regression analysis. In both

cases, the input considered the k-the closet training in the feature space. In simple terms, the

structure of this is basically determent and it is proving very useful in the real world. This is not

basically meant to obey te assumptions produced by some of the other algorithms. In this regard,

this algorithms can be one of the choices used when there is a serious distribution of data. This

algorithms is also called lazy algorithms because according to the analysis, it side not use the

training data and does not point to many particular generalization. There is not explicit training

or if there is any it is very minimal. The output seen in this case is a class based membership

which means that is can be predicted. In this algorithm, the objects is basically classified a

majority of votes complied with the neighbors and is assigned to class of the most common

among the group. It is also used to regression. In most cases, the output is value of the object in

question and due to this, the average value is received from the K nearest neighbor.

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser

Part 2.

A confusion matrix is defined as a techniques used in summarizing the performance of the

classification algorithms. When we consider the accuracy of the data presented without looking

at the factor observed, we might get a misleading results because other resources may not be

considered. Presenting a confusion matrix has given us a better idea of what the classification

model is and we have given a right data as it is marking. When data is more than 2 classes the

accuracy according to the observation is 80%. We are not sure whether this value is true because

it is predicted and it may not give the model of the entire learning. When data lacks even number

as this particular data, accuracy may be achieve at 90% level however this is not considered as a

good level either because it belongs to one class that always predicting the most common class

values.

Confusion matrix in this case has been used to predict the results of the problems. The project

summarizes the number of correct and incorrect predictors with the count values and Brocken

values and they are taken back to each and every class (Ohsaki & Ralescu et al., 2017). This is

the main idea of the confusion matrix. The confession matrix also has shown how the

classification model is confused when main predictions. It also gives a clear insight which is not

only based on the errors committed by the classifier but is more importantly the type of error

which is currently being made.

The weka machine displays a confusion matrix automatically when the estimations are done.

This modeling is done on weka explorer interface. In weka, the confusion matric is listed at the

bottom and the entire presentation is also presented. For the confusion matrix, different letter are

assigned to ascertain the predicted values. In most cases, the problem of prediction models is to

A confusion matrix is defined as a techniques used in summarizing the performance of the

classification algorithms. When we consider the accuracy of the data presented without looking

at the factor observed, we might get a misleading results because other resources may not be

considered. Presenting a confusion matrix has given us a better idea of what the classification

model is and we have given a right data as it is marking. When data is more than 2 classes the

accuracy according to the observation is 80%. We are not sure whether this value is true because

it is predicted and it may not give the model of the entire learning. When data lacks even number

as this particular data, accuracy may be achieve at 90% level however this is not considered as a

good level either because it belongs to one class that always predicting the most common class

values.

Confusion matrix in this case has been used to predict the results of the problems. The project

summarizes the number of correct and incorrect predictors with the count values and Brocken

values and they are taken back to each and every class (Ohsaki & Ralescu et al., 2017). This is

the main idea of the confusion matrix. The confession matrix also has shown how the

classification model is confused when main predictions. It also gives a clear insight which is not

only based on the errors committed by the classifier but is more importantly the type of error

which is currently being made.

The weka machine displays a confusion matrix automatically when the estimations are done.

This modeling is done on weka explorer interface. In weka, the confusion matric is listed at the

bottom and the entire presentation is also presented. For the confusion matrix, different letter are

assigned to ascertain the predicted values. In most cases, the problem of prediction models is to

have a good performance making a prediction which is based on a new data. This makes it very

important to use a more reliable techniques to help evaluate the models that is to be evaluated.

The observation provides a suggestion that the more reliable the estimation is, the more

performance and the further one can go on with the estimation. This also dictates how confident

the operational will be.

Is there a difference in performance between the algorithms?

From the observation, there is a difference in the performance in the algorithms. The difference

is evidence when using the K-nearest neighbor which presents a suggestion that the nearest

neighbor will be selected. This is proves to give a more accurate information in comparison to

the rest of the algorithms (Deng & Mahadevan, 2016). While the main aim of a decision tree is to

solve every problem using a tree replacement representation. Each and every internal nodes

corresponds to an attribute and each and every leaf will correspond to a class label. When using a

decision to predict the label for a god record, the process starts with the roots of the tree. We do a

comparison of the root t attribute, the K nearest neighbor will be more concentrative on the issue

of the nearest tree.

important to use a more reliable techniques to help evaluate the models that is to be evaluated.

The observation provides a suggestion that the more reliable the estimation is, the more

performance and the further one can go on with the estimation. This also dictates how confident

the operational will be.

Is there a difference in performance between the algorithms?

From the observation, there is a difference in the performance in the algorithms. The difference

is evidence when using the K-nearest neighbor which presents a suggestion that the nearest

neighbor will be selected. This is proves to give a more accurate information in comparison to

the rest of the algorithms (Deng & Mahadevan, 2016). While the main aim of a decision tree is to

solve every problem using a tree replacement representation. Each and every internal nodes

corresponds to an attribute and each and every leaf will correspond to a class label. When using a

decision to predict the label for a god record, the process starts with the roots of the tree. We do a

comparison of the root t attribute, the K nearest neighbor will be more concentrative on the issue

of the nearest tree.

Screenshots from wek

Need help grading? Try our AI Grader for instant feedback on your assignments.

References

Ting, K. M. (2017). Confusion matrix. In Encyclopedia of Machine Learning and Data Mining

(pp. 260-260). Springer, Boston, MA.

Ohsaki, M., Wang, P., Matsuda, K., Katagiri, S., Watanabe, H., & Ralescu, A. (2017).

Confusion-matrix-based kernel logistic regression for imbalanced data classification. IEEE

Transactions on Knowledge and Data Engineering, 29(9), 1806-1819.

Deng, X., Liu, Q., Deng, Y., & Mahadevan, S. (2016). An improved method to construct basic

probability assignment based on the confusion matrix for classification problem. Information

Sciences, 340, 250-261.

Tsirigos, K. D., Peters, C., Shu, N., Kรคll, L., & Elofsson, A. (2015). The TOPCONS web server

for consensus prediction of membrane protein topology and signal peptides. Nucleic acids

research, 43(W1), W401-W407.

Ting, K. M. (2017). Confusion matrix. In Encyclopedia of Machine Learning and Data Mining

(pp. 260-260). Springer, Boston, MA.

Ohsaki, M., Wang, P., Matsuda, K., Katagiri, S., Watanabe, H., & Ralescu, A. (2017).

Confusion-matrix-based kernel logistic regression for imbalanced data classification. IEEE

Transactions on Knowledge and Data Engineering, 29(9), 1806-1819.

Deng, X., Liu, Q., Deng, Y., & Mahadevan, S. (2016). An improved method to construct basic

probability assignment based on the confusion matrix for classification problem. Information

Sciences, 340, 250-261.

Tsirigos, K. D., Peters, C., Shu, N., Kรคll, L., & Elofsson, A. (2015). The TOPCONS web server

for consensus prediction of membrane protein topology and signal peptides. Nucleic acids

research, 43(W1), W401-W407.

1 out of 9