5 Major Classes of Learning Problems | Machine Learning

Major Classes of machine learning problems based on the specific nature of the prediction or decision-making task.

Understanding different learning problems is crucial for developing effective machine learning solutions. Each problem class addresses unique challenges and requires tailored approaches, making it essential for practitioners to choose the right methodology for their specific task.

This article introduces the major learning problem classes :

1. Classification
2. Regression
3. Ranking
4. Clustering
5. Dimensionality Reduction (or Manifold learning)

1. Classification

Classification involves assigning input data to predefined categories or classes. It is a type of supervised learning. In classification the classes are predicted for the given dataset.

Examples of classification task include

1. Fraud Detection

• Fraud detection involves identifying potentially fraudulent activities or transactions based on patterns and anomalies in the data. For example, in finance, fraud detection systems analyze transaction records to flag suspicious activities.

2. Spam Detection

• Spam detection aims to distinguish between legitimate and unwanted emails or messages. Email providers and messaging platforms use machine learning algorithms to filter out spam messages and prevent them from reaching users’ inboxes.

3. Image Classification

• Image classification involves assigning labels or categories to images based on their visual features. For instance, in medical imaging, image classification can be pivotal in diagnosing diseases from X-ray or MRI images.
• Accurate image classification plays a crucial role in medical diagnosis, enabling healthcare professionals to identify abnormalities and make informed treatment decisions.
• Another example is shown in the following picture. When we train the model on some images of animals and test it with a specific pictures it predicts that animal name.

2. Regression

Regression predicts a continuous outcome or value. It is used when the target variable is numeric.

Examples of regression tasks include

1. Prediction of stock values

2. Prediction of weather

3. Prediction of house prices

3. Ranking

Ranking means ordering based on some specific criterion. Various ranking problem arise in context of the design of information extraction and specific queries.

Examples of ranking problem includes

1. Search engine (For example, give a web page based on some specific search criteria.)
2. Recommendation system (For example: based on users purchase history suggest new products that user might like)

4. Clustering

Clustering groups similar instances together based on certain criteria without predefined categories. It partition items into homogeneous regions. It is often performed to analyze very large datasets .

Example of clustering includes:

1. Anomaly detection
2. Image segmentation
3. Social network analysis
4. Market segmentation

5. Dimensionality Reduction

Dimentionality reduction transforms an initial represenation of items into a lower-dimensional representation of these items while preserving some properties of the initial representation. Reducing the number of features is essential for better model performance. Dimensionality reduction enhances model interpretability and training efficiency.

Dimentionality reduction PCA for two components

Example includes:

1. preprocessing digital images in computer vision tasks

Conclusion

The primary goal of machine learning is to create precise forecasts for unfamiliar items and develop algorithms that are both effective and resilient, capable of generating these predictions even when dealing with extensive challenges.

References

1. “Introduction to Machine Learning with Python” by Andreas C. Müller and Sarah Guido
2. “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron