Machine Learning Models: A Complete Guide to Classification Approaches

Introduction to Machine Learning Models

Machine learning models (algorithms) can be classified in multiple ways. Though organizing these models is complex, several useful classification approaches help us understand them better.

Each classification method emphasizes different model characteristics and serves important purposes:

Choose the appropriate model for specific problems (e.g., regression, classification, clustering)

Understand the data requirements and underlying assumptions of each model

Evaluate computational complexity and efficiency during training and inference

Compare performance between similar models within the same category

Facilitate interpretability and explainability of the model in its application context

However, too many classifications can create confusion rather than clarity. For this reason, we’ll focus on two main classifications while briefly summarizing the others.

These two main classification approaches for machine learning algorithms—the “paradigm-based” approach and the “problem-based” approach—are complementary. They examine two distinct yet equally crucial aspects of understanding these tools.

Machine Learning Models: A Paradigm-Based Approach

Supervised Learning

Supervised learning is one of the most common approaches in machine learning. This method trains a model on a dataset containing input features (independent variables) and corresponding target outcomes (dependent variables). The model’s task is to learn the relationship between these features and targets. Once trained, it can make predictions or classifications for new data where the target is unknown. This approach is powerful because it uses known outcomes to guide learning.

The following algorithms employ this approach:

Linear and logistic regression
Decision trees
Support Vector Machines (SVMs)
Neural networks

This type of algorithm is commonly used for visual recognition, medical diagnostics, and asset valuation.

Unsupervised Learning

In contrast to supervised learning, unsupervised learning uses datasets without predefined outcome information for model training.

The model’s task is to identify relationships and patterns within the provided data, grouping them into homogeneous sets.

This category includes:

Clustering algorithms (e.g., K-Means and DBSCAN)
Dimensionality reduction algorithms (e.g., PCA, t-SNE)

These techniques are applied in dimensionality reduction, customer segmentation, and exploratory data analysis.

Semi-supervised Learning

This type of learning occurs when only a portion of the available data contains target information, and obtaining the missing information would be too costly or complicated.

In such cases, classification is performed based on the limited available information.

Typically, the algorithms used are variations of unsupervised learning techniques.

Reinforcement Learning

This paradigm differs significantly from the previously discussed approaches. It involves an algorithm interacting with an environment, receiving rewards or punishments based on its actions. Learning is thus guided by the environment’s feedback to the algorithm’s decisions.

Examples of reinforcement learning algorithms include:

Q-learning
Deep Q Network

These algorithms find applications in robotic control, game strategy development, and management of dynamic environments such as portfolio optimization.

Self-supervised Learning

This approach is similar to unsupervised learning, but the algorithm creates its own pseudo-labels (also known as pseudo-targets or pseudo-outcomes).

Self-supervised learning is widely used in deep neural networks and includes models such as:

BERT
GPT

These models are primarily applied in natural language processing and image recognition tasks.

Generative Models

These sophisticated models are designed to learn the underlying patterns and distributions of training data, enabling them to create entirely new data samples that closely resemble the initial dataset. By capturing the essential characteristics and statistical properties of the input data, these models can generate synthetic but realistic outputs.

Examples of generative algorithms include:

GANs (Generative Adversarial Networks), which use a competitive training process between a generator and discriminator to create highly realistic synthetic data
VAEs (Variational Autoencoders), which learn compressed representations of data while maintaining the ability to generate new samples through probabilistic modeling

Generative models have revolutionized creative applications, finding extensive use in generating synthetic text content, creating photorealistic images, producing high-quality video sequences, and even composing music. Their ability to understand and replicate complex patterns has made them invaluable tools in various creative and practical applications.

Machine Learning Model: A Problem-based Approach

Regression

Regression algorithms predict continuous variables (such as prices, temperatures, and other values expressed on a continuous scale). These supervised algorithms are used for estimating numerical outcomes.

This group includes:

Simple regression
Polynomial regression

In the medical field, regression algorithms help predict continuous values—such as hospital stay duration and blood glucose levels.

Classification

Unlike regression, classification algorithms predict discrete, categorical variables. These supervised algorithms sort data into distinct categories.

This group includes:

Support Vector Machines (SVM)
Decision trees

Classification algorithms are widely used in clinical settings, particularly in automated diagnosis systems that analyze radiological images to distinguish between benign and malignant tumors.

Ensemble

Ensemble algorithms form a unique category, applicable to regression and classification problems. They improve predictions by combining multiple models.

Examples include:

Bagging algorithms (e.g., Random Forest)
Boosting algorithms (e.g., Gradient Boosting Machine, XGBoost)

Ensemble models find extensive applications in clinical settings, particularly in enhancing clinical decision support systems and risk assessment.

Unsupervised

These algorithms are identical to the unsupervised category discussed in the previous classification. They work with unlabeled data to discover patterns and structures.

Unsupervised algorithms help identify patient subgroups with similar characteristics—for example, grouping patients with metabolic syndrome or stratifying cancer patients based on tumor characteristics.

In the “problem-based” classification, regression, classification, and ensemble categories fall under the Supervised Learning category of the “paradigm-based” classification. The “unsupervised” category, however, remains consistent across both classification methods.

Summary of “paradigm-based” – “problem-based” classifications

By Paradigm	By Problem
Supervised Learning	Regression
Unsupervised Learning	Classification
Semi-supervised Learning	Ensemble
Reinforcement Learning	Unsupervised
Self-supervised Learning
Generative Models

Alternative Classifications of Machine Learning Models

As mentioned earlier, there are numerous other classifications of machine learning algorithms. However, these additional classifications don’t significantly enhance our understanding beyond what we’ve already explored in depth.

Nevertheless, these alternative classifications can be valuable in specific contexts. They often highlight particular requirements or considerations, such as the type of data being processed or the computational resources needed.

Let’s briefly recap additional classification approaches:

Model type: probabilistic, linear, non-linear, distance-based, and generative
Computational complexity: lightweight or intensive
Data type: tabular, sequential, or unstructured
Interpretability: interpretable or black-box
Optimization method: gradient-based, heuristic, or exact
Training mode: batch, online, or incremental learning
Application domain: computer vision, natural language processing, recommendation systems, or anomaly detection

Conclusion

The machine learning model classifications presented here provide complementary perspectives for understanding and organizing these powerful tools. The paradigm-based classification illuminates how models learn—whether through supervised, unsupervised, or reinforcement learning—while the problem-based classification helps us select the most appropriate model for specific tasks like regression or classification. This dual framework enables us to identify the ideal model from a broad range for our specific analytical needs.

This course will be structured according to the “problem-based” classification when describing various machine learning systems.