Analytical Statistics Techniques of Classification and Regression in Machine Learning

Kumar, Pramod; Ambekar, Sameer; Kumar, Manish; Roy, Subarna

doi:10.5772/intechopen.84922

Cited by 3 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The provided code will generate output through a Confusion Matrix and an evaluation model diagram. The Confusion Matrix shows the number of correct and incorrect prediction values compared to the actual data, with the X-axis representing the predicted labels and the Y-axis representing the actual data labels (Kumar & Batut, 2019). The values obtained from this Confusion Matrix can be used to calculate various evaluation metrics such as accuracy, precision, recall, and F1-Score to assess the performance of the classification model (Wilianto, 2021).…”

Section: Discussionmentioning

confidence: 99%

Phrase Detection's Impact on Sentiment Analysis of Public Opinion and online Media Toward Political Figures

Nurodin,

Yan Puspitarani

2024

J. Ris. Inform.

View full text Add to dashboard Cite

Public opinion of political figures and policy significantly impacts general elections. Sentiment analysis, as a method to comprehend opinion and emotion in texts, requires the step of text preprocessing to improve data quality. However, textual data often encounters irrelevant words and ambiguous language. These conditions can impact the accuracy of sentiment analysis. Given the significance of precisely interpreting public opinion toward political figures, these issues may result in biased or inaccurate sentiment analysis outcomes. Irregular punctuation or unclear language can disturb the text's intended context, compromising sentiment analysis quality. Additionally, irrelevant words can obscure the focus of the analysis, causing fundamental changes in the original text's meaning. This research focuses on the impact of a specific preprocessing technique, namely Phrase Detection with N-Gram, on sentiment analysis of political figures. By applying this method, the study aims to detail the effects of using Bigram, Trigram, and Unigram on the quality of sentiment analysis, particularly in the context of political figures on Twitter and online media articles. This research indicates that using Bigram in Phrase Detection provides more significant results than Trigram and Unigram for most political figures at Twitter, with the highest accuracy score of 88,23%. Sentiment analysis of articles in online media also indicates various results depending on the type of N-Gram. The results indicate that using N-gram phrase detection can influence the accuracy of sentiment analysis, and the resulting accuracy values are pretty high.

show abstract

Section: Discussionmentioning

confidence: 99%

Phrase Detection's Impact on Sentiment Analysis of Public Opinion and online Media Toward Political Figures

Nurodin,

Yan Puspitarani

2024

J. Ris. Inform.

View full text Add to dashboard Cite

show abstract

“…The complex and non-linear effects between these variables are explored, and a heart attack prediction model is created based on data sets [10][11]. The assessment of heart attack risk relies on various risk factors for cardiovascular disease to predict an individual's likelihood of having an acute heart attack [12]. In this way, the corresponding intervention measures are taken to reduce the influence of risk factors, prevent and reduce the occurrence of such clinical events in a timely manner, and improve the health of the whole community.…”

Section: Al-rafidain Journal Of Computer Sciences and Mathematics (Rjcm)mentioning

confidence: 99%

Hybridization of Swarm for Features Selection to Modeling Heart Attack Data

Shakir¹,

Saleh²

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

View full text Add to dashboard Cite

Predicting heart attacks using machine learning is an important topic. Medical data sets contain different features, some of which are related to the target group for prediction and some are not. In addition, the data sets are excessively unbalanced, which leads to the bias of machine learning models when modeling heart attacks. To model the unbalanced heart attack data set, this paper proposes the hybridization of Particle swarm optimization (PSO), BAT, and Cuckoo Search (CS) to select the features and adopt the precision for minority classes as a fitness function for each swarm to select the influential features. In order to model the data, set in which the features were selected, it was proposed to use the boosting (Catboost) as a classifier for predicting heart attacks. The proposed method to select features has been compared with each of the three swarms, and the Catboost algorithm has been compared to traditional classification algorithms (naive Bayes, decision trees). The study found that the proposed method of hybridization of the results of the (PSO, BAT, and BCS) algorithms in selecting features is a promising solution in the field of selecting features and increases the accuracy of the system, and that traditional machine learning models are biased in the case of unbalanced data sets and that selecting the important features according to the target class has an impact on the performance of the models, In addition, the definition of hyperparameters reduces the bias of the selected model. The final model achieved an overall accuracy of 96% on the Accuracy scale and 56% on the Precision scale for the minority class

show abstract

“…Feature selection aims at selecting the relevant study-related attributes for the target class. In general, classification models can be based on machine learning or statistical models [16]. The machine learning based models include decision trees (DT), random forest (RF), artificial neural networks (ANN), k nearest neighbour (KNN), cased based reasoning (CBR), support vector machines (SVM), AdaBoost, Stochastic gradient descent (SGD), other ensemble and boosting classifiers.…”

Section: Introductionmentioning

confidence: 99%

Class probability distribution based maximum entropy model for classification of datasets with sparse instances

Saravanan¹,

Anandhi²,

Srividya³

2023

ComSIS

View full text Add to dashboard Cite

Due to the digital revolution, the amount of data to be processed is growing every day. One of the more common functions used to process these data is classification. However, the results obtained by most existing classifiers are not satisfactory, as they often depend on the number and type of attributes within the datasets. In this paper, a maximum entropy model based on class probability distribution is proposed for classifying data in sparse datasets with fewer attributes and instances. Moreover, a new idea of using Lagrange multipliers is suggested for estimating class probabilities in the process of class label prediction. Experimental analysis indicates that the proposed model has an average accuracy of 89.9% and 86.93% with 17 and 36 datasets. Besides, statistical analysis of the results indicates that the proposed model offers greater classification accuracy for over 50% of datasets with fewer attributes and instances than other competitors.

show abstract

Analytical Statistics Techniques of Classification and Regression in Machine Learning

Cited by 3 publications

References 7 publications

Phrase Detection's Impact on Sentiment Analysis of Public Opinion and online Media Toward Political Figures

Phrase Detection's Impact on Sentiment Analysis of Public Opinion and online Media Toward Political Figures

Hybridization of Swarm for Features Selection to Modeling Heart Attack Data

Class probability distribution based maximum entropy model for classification of datasets with sparse instances

Contact Info

Product

Resources

About