Results of training as well as validation: The matrix of confusion of all ML models.

Results of training as well as validation: The matrix of confusion of all ML models.

<p>(a) Stacking, (b) AdaBoost, (c) Bagging, (d) K-NN, (e) LR, (f) MLP, (g) RF, (h) SGD, (i) Voting, (j) XGBoost.</p>

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Bibliographic Details
Main Author: Khandaker Mohammad Mohi Uddin (13124356) (author)
Other Authors: Hasibul Hamim (19797121) (author), Mst. Nishat Tasnim Mim (19797124) (author), Arnisha Akhter (16323159) (author), Md Ashraf Uddin (14855902) (author)
Published: 2024
Subjects:
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
using mathematical visualization
stochastic gradient descent
social media platforms
safeguard psychological wellness
reverse document frequency
online bullying could
natural language processing
free online environment
eliminate online harassment
combined two sets
become much easier
cnn ), convolutional
deep neural networks
binary classification model
categorize bengali comments
000 bengali comments
bidirectional long short
34 %, precision
mlp ), k
bengali comments
long short
lstm ),
hybrid model
deep learning
33 %,
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writing aims
term memory
reaching consequences
rapid adoption
producing 94
nearest neighbors
machine learning
layer perceptron
large number
large amount
label class
f1 score
evaluation accuracy
earlier stage
different points
detected quickly
count vectorizer
contemporary web
based approach
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Description
Summary:<p>(a) Stacking, (b) AdaBoost, (c) Bagging, (d) K-NN, (e) LR, (f) MLP, (g) RF, (h) SGD, (i) Voting, (j) XGBoost.</p>

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