Hello Md. Faisal-E-Alam,

🎯 Goal: Learn Machine Learning for Ordinal Data

Ordinal data means values that have a natural order, but the differences between values aren't necessarily equal. Example: survey ratings like "Poor", "Fair", "Good", "Very Good", "Excellent". These are not just categories, and not quite numbers either — so they require special care.

🧩 Step-by-Step Learning Path

✅ Step 1: Understand What Ordinal Data Is

Start by learning the different types of data:

• Nominal data – categories with no order (e.g., colors: red, blue)
• Ordinal data – categories with order (e.g., satisfaction ratings)
• Interval/Ratio data – numerical values with meaningful differences

🧠 Key idea: Ordinal values have rank, but we don’t know exactly how far apart they are.

📚 Step 2: Learn Basic Machine Learning Concepts

Before handling ordinal data, it's important to understand the foundation of ML:

• What is a feature and a label
• What is classification vs. regression
• How do we train and test a model
• What is a loss function or error metric

🛠️ Suggested beginner courses:

• Coursera – Andrew Ng’s Machine Learning (highly beginner-friendly)
• Google Machine Learning Crash Course
• Kaggle’s ML Intro Course

📌 Start with small projects, like predicting housing prices or classifying flowers. These help build your confidence.

🔢 Step 3: Learn How Ordinal Data Is Different in ML

Most ML models treat data as:

• Numeric (e.g., height, weight)
• Categorical (e.g., city names)

But ordinal data is in between: it’s categorical, but with order. So:

• You should not one-hot encode ordinal data (this loses order).
• You should not treat it like plain numbers either (the model might assume wrong distances).

🎓 Learn about:

• Label Encoding: Convert categories like ["Poor", "Fair", "Good"] to [1, 2, 3]
• Ordinal-Specific Models (explained in the next steps)

📊 Step 4: Learn ML Models for Ordinal Data

There are special models and techniques for ordinal classification:

🔸 A. Ordinal Logistic Regression

• Simple and interpretable
• Great for small datasets
• Available in Python (statsmodels) and R (MASS, ordinal)

🔸 B. Tree-based Models

• Like XGBoost, LightGBM, or CatBoost
• These are powerful and often used in practice
• Can handle ordinal data well when trained carefully

🧪 Tip: For these models, label encode your ordinal target and optionally use custom loss functions to teach the model the order matters.

🔸 C. Neural Networks for Ordinal Data

• More advanced, but useful for complex problems
• You’ll need to use custom loss functions that respect order (like ordinal cross entropy)

🧰 Step 5: Tools and Libraries You Can Use

Here are beginner-friendly tools and Python libraries:

Task Tool / Library Description Load and clean data pandas, numpy Work with tables (CSV, Excel, etc.) Train simple models scikit-learn Easy library to train models Ordinal Logistic Regression statsmodels, mord Simple and interpretable models Tree-based models LightGBM, XGBoost Fast, powerful models for larger data Vector search (for documents) FAISS, Haystack If you're analyzing text or document data Plot and analyze matplotlib, seaborn Visualize your results

📄 Step 6: Practice on Real Data

Hands-on practice is the best way to learn.

Datasets with ordinal targets:

• Kaggle: Customer satisfaction
• UCI Machine Learning Repository
• Create your own: Try collecting feedback with ratings like 1–5 and train a model to predict them.

🔍 Step 7: Evaluate the Right Way

For ordinal classification, you should use evaluation metrics that consider order, such as:

• Mean Absolute Error (MAE) – how far off the predictions are
• Cohen’s Kappa / Quadratic Weighted Kappa – measures agreement with correct answers
• Spearman’s Rank Correlation – if predicting rankings

Avoid accuracy alone—it doesn’t capture how "wrong" the prediction is in terms of order.

📖 Step 8: Explore Further Learning and Research

As you progress, you can explore:

• Research papers like: “Ordinal Regression Models: A Review” – Zhang & Zhou “Deep Ordinal Regression Networks” – Niu et al.
• Tutorials on: Ordinal regression with XGBoost [Custom loss functions in PyTorch/TensorFlow]

🔚 Final Advice

Start simple:

• Learn ML basics using easy tutorials and datasets.
• Understand how ordinal data is special.
• Practice with small projects (like predicting survey results).
• Gradually explore more advanced models and tools.

🎯 Once you’re comfortable, you’ll be able to apply ML to ordinal problems in areas like:

• Customer satisfaction
• Medical diagnosis stages
• Credit scoring
• Academic grading

You need to first understand what ordinal data is: it is composed of ordered groups lacking defined distances between them. Then, you can learn how to apply machine learning to examine ordinal scale data. Tree-based models such as LightGBM with the appropriate encoding or models designed for ordinal outcomes, such ordinal logistic regression, should be used. Use integer encoding maintaining the order rather than one-hot encoding. Practice using datasets with ordered objectives; test your models using mean absolute error or quadratic weighted kappa. Start with Python tools including mord, scikit-learn, and statsmodels.

Let me suggest a paper that may help you to understand the ML concepts and also find relevant references and finding for your research.

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