Classification Models, Deep Learning, Exploratory Data Analysis, Feature Engineering, Machine Learning - Data Mining1 Comment

Case study: Machine Learning and Deep Learning for Knowledge Tracing in Programming Education

May 8, 2022May 14, 2022 Tung.M.Phung

Applying Machine Learning and Deep Learning to solve the Knowledge Tracing problem in the context of Programming classrooms. Continue reading Case study: Machine Learning and Deep Learning for Knowledge Tracing in Programming Education

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Ensemble: Bagging, Random Forest, Boosting and Stacking

May 4, 2020July 31, 2020 Tung.M.Phung

An ensemble of trees (in the form of bagging, random forest, or boosting) is usually preferred over one decision tree alone. Continue reading Ensemble: Bagging, Random Forest, Boosting and Stacking

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Binary Classification Evaluation Summary

January 15, 2020July 31, 2020 Tung.M.Phung

This article attempts to summarize the popular evaluation metrics for binary classification problems. Continue reading Binary Classification Evaluation Summary

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Precision-Recall curve: an overview

January 12, 2020July 31, 2020 Tung.M.Phung

We introduce an alternative for the ROC: the Precision-Recall curve (PR-curve), which is a more reliable measurement for the cases when Positive samples are rare. Continue reading Precision-Recall curve: an overview

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ROC curve and AUC: a comprehensive overview

January 10, 2020July 31, 2020 Tung.M.Phung

The well-known ROC curve plot, the Area Under the ROC Curve (AUC), and its variants. Continue reading ROC curve and AUC: a comprehensive overview

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How to read the Confusion Matrix

January 8, 2020July 31, 2020 Tung.M.Phung

The Confusion Matrix is a square table representing the predictions of a classification model. Continue reading How to read the Confusion Matrix

Classification Models, Machine Learning - Data Mining6 Comments

Information Gain, Gain Ratio and Gini Index

January 4, 2020July 31, 2020 Tung.M.Phung

Information Gain, Gain Ratio and Gini Index are the three fundamental criteria to measure the quality of a split in Decision Tree. Continue reading Information Gain, Gain Ratio and Gini Index

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Logistic Regression: Advantages and Disadvantages

December 28, 2019July 31, 2020 Tung.M.Phung

In the previous blogs, we have discussed Logistic Regression and its assumptions. Today, the main topic is the theoretical and empirical goods and bads of this model. Continue reading Logistic Regression: Advantages and Disadvantages

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Naive Bayes classifier: a comprehensive guide

December 25, 2019July 31, 2020 Tung.M.Phung

In this blog post, we show and explain the Bayes formula, how to build a Naive Bayes classifier, its assumptions, strengths, and weakness. Continue reading Naive Bayes classifier: a comprehensive guide

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Assumptions of Logistic Regression

December 22, 2019July 31, 2020 Tung.M.Phung

When these requirements, or assumptions, hold true, we know that our Logistic model has expressed the best performance it can. Continue reading Assumptions of Logistic Regression

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