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Machine-learning classification is a supervised task: a model learns from examples with known categories and uses what it learned to assign categories to new cases. For example, a spam filter can learn from messages labeled “spam” or “not spam.” Classification predicts labels; regression, by contrast, predicts numerical values.
What classification means
A labeled training example contains input information and a known target category. The model uses a collection of these examples to learn a rule for relating inputs to labels. Once fitted, it can apply that rule to an unseen case.
In the email illustration, message features are the inputs and “spam” or “not spam” is the label. The model does not need to memorize every training message; its purpose is to generalize to messages it has not seen. Depending on the method, a classifier may return a label directly or provide a score or probability that can help determine how to assign one.
Classification versus regression
Both are supervised prediction tasks, but their targets differ. A classifier predicts a category, such as a type of flower or whether a transaction is flagged. A regression model predicts a numerical value, such as a house price or the expected time until delivery. The distinction is about the question being answered, not whether the underlying data contains numbers: a classifier may use numerical inputs while predicting a category.
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Common classifier families
Introductory machine-learning materials cover several approaches. These examples are representative, not exhaustive, and do not establish the contents of any particular DM2 course syllabus.
- Linear and logistic models: Use a comparatively simple mathematical relationship between input features and the target. Logistic regression is commonly used for classification despite “regression” in its name.
- Bayesian methods, including Naive Bayes: Use probability-based reasoning. Naive Bayes makes simplifying assumptions about how features relate, which can make it practical while limiting how well those assumptions fit some data.
- Nearest neighbors: Assign a label based on nearby examples in the training data. What counts as “near” depends on how the features are represented and compared.
- Decision trees: Apply a sequence of feature-based decisions to reach a category. Their branching structure can make individual decisions easier to inspect, though a tree may become complex.
- Support vector classification: Seeks a boundary that separates categories, with variants and settings that affect the boundary and model behavior.
How to compare methods for a task
No classifier is best for every dataset or objective. Introductory course materials name different methods but do not provide a shared empirical benchmark that would support a universal ranking. A useful comparison starts with the task, the available evidence, and the consequences of mistakes.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
| Question | Why it matters |
|---|---|
| What labels must the model produce? | Binary classification chooses between two categories; multiclass classification chooses among more than two. Multilabel tasks allow more than one label to apply to a case. The label structure affects how a model and its outputs should be configured. |
| How important is interpretability? | A decision tree may expose a sequence of decisions, while other methods can be harder to explain case by case. Interpretability needs depend on who must review or act on predictions. |
| What assumptions does the method make? | Methods differ in how they treat feature relationships, decision boundaries, and similarity. A method whose assumptions fit the data may be a better candidate than one that is more complex but poorly matched. |
| What data and computation are available? | Training data size, feature representation, and the cost of fitting or applying a model can affect feasibility. The named method families do not have one fixed resource requirement across all implementations and datasets. |
| What does each type of error cost? | A false positive assigns a case to a category when it does not belong; a false negative misses a case that does. Their relative consequences should guide how performance is assessed and how a prediction threshold is chosen where applicable. |
Why evaluation is part of classification
A model’s performance on its training examples alone does not show how well it will classify new cases. Evaluation is therefore part of the supervised-learning workflow: assess predictions against known labels using data that can indicate how the model generalizes. The appropriate assessment depends on the label structure and on which errors matter most. There is no single metric or benchmark established for every classification task.
University course descriptions and syllabi discuss classification methods and assessment as introductory machine-learning topics, but they do not establish that a particular sequence or set of models belongs to the DM2 course. The exact course identity and syllabus are not confirmed by those materials.
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