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Machine learning uses data structures to represent and organize information, and algorithms to search data, learn patterns, or optimize a model. There is no canonical list of the five most common: the examples below are a representative introduction, not a ranking. They cover feature matrices, trees, graphs, hashing, and k-means, while distinguishing structures from methods.
How data structures and algorithms fit into machine learning
A data structure describes how data is represented, stored, or indexed. An algorithm is a procedure that operates on data—for example, to find nearby examples, divide observations into groups, or adjust a model to reduce its error. A tree can be either a learned model or a search index, depending on how it is built and used.
These choices are part of a broader workflow: prepare data, represent it in a form a method can use, then apply a learning or search procedure. Scikit-learn documents a range of supervised and unsupervised methods in its user guide; no single representation or algorithm suits every task.
Five representative examples
1. Arrays and feature matrices — data representations
Numerical samples are commonly represented in arrays. A feature matrix organizes a dataset as rows of examples and columns of features—for instance, rows might represent houses and columns might contain floor area, age, and location encoded numerically. The exact representation depends on the library and data type.
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Preparing the matrix is a practical part of machine learning: the chosen features and their representation affect what information a method can use. The format itself is not a learning algorithm; it is a way to supply data to one.
2. Trees — learned models or search indexes
“Tree” describes a branching structure, not one particular machine-learning task. A decision tree is a supervised model that makes predictions using feature-based split rules. Scikit-learn describes decision trees as “a non-parametric supervised learning method used for classification and regression.” Its documentation explains that these models recursively partition feature space: scikit-learn decision trees.
A KD tree has a different job: it indexes points in a multidimensional space to support nearest-neighbor lookup. Scikit-learn documents brute-force and tree-based neighbor searches, while noting that KD-tree efficiency declines as dimensionality grows. A KD tree may therefore help in lower-dimensional cases, but it is not a universal shortcut for every dataset or search workload: scikit-learn nearest neighbors.
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3. Graphs — relationship representations
A graph represents entities as nodes and relationships between them as edges. In machine learning, a graph can express connections among samples, such as links between nearby observations. This makes relationships explicit rather than representing each sample only as an independent row.
Graph-based methods are one useful family, not a universal internal format for ML. Scikit-learn’s clustering comparison discusses nearest-neighbor graphs and graph distance in connection with methods including spectral clustering and affinity propagation: scikit-learn clustering methods compared.
4. Hashing — a mapping technique
Hashing maps a value to a bucket index. For categorical data, this can map many possible category values into a fixed set of buckets, which can be useful when the set of categories is large. Google’s machine-learning glossary describes this use of hashing: Google for Developers machine-learning glossary.
Because the available buckets may be fewer than the possible values, distinct categories can land in the same bucket. That collision is a tradeoff: hashing provides a bounded set of indices but does not guarantee a unique bucket for every category. Hashing is a technique, not a general-purpose data structure in the same sense as an array, tree, or graph.
5. K-means — a clustering algorithm
K-means groups numerical points around cluster centroids by minimizing distances to those centroids. It is an algorithm, not a data structure. Its usefulness depends on whether the data’s geometry and scale make distance-to-centroid grouping meaningful. Scikit-learn’s clustering guide compares k-means with methods that handle different clustering situations: scikit-learn clustering.
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Other algorithms you will encounter
Nearest-neighbor search
Nearest neighbors retrieves or predicts from examples close to a query point. A straightforward implementation can compare the query with stored examples directly; indexed options such as KD trees can reduce search work in suitable cases. The practical choice depends on setup cost, sample count, dimensionality, and whether an index is effective for the data. Scikit-learn documents brute-force and tree-based approaches in its nearest-neighbor guide.
Decision-tree learning
In decision-tree learning, the algorithm selects feature splits that form a tree-shaped model. The tree is the resulting model structure; the procedure for choosing splits is the learning algorithm. Decision trees support classification and regression, as described in the scikit-learn documentation.
Gradient descent
Gradient descent is an optimization algorithm used when fitting models: it adjusts model parameters in relation to a loss function. Google’s Machine Learning Crash Course teaches it alongside loss and hyperparameter tuning: Google Machine Learning Crash Course. It is an algorithm, not a storage format, and its presence in a workflow does not by itself establish that it is the best optimizer for every model or task.
How to choose among them
First identify the task and the shape of the data; then choose a representation or method that matches. These examples solve different problems, so they are not interchangeable alternatives.
- Representing tabular numerical samples: use an array or feature matrix suited to the library and data type.
- Predicting a class or value with split rules: consider a decision tree; its learned structure is distinct from a KD-tree index.
- Finding nearby examples: compare direct search with an index. KD trees are most relevant in lower-dimensional settings, and their efficiency decreases as dimensionality grows.
- Representing relationships among samples: a graph can encode those links for methods that use them, but many ML workflows do not require a graph.
- Grouping points around centroids: consider k-means when distance-based cluster geometry suits the data; for very large sample counts, scikit-learn documents mini-batch k-means as a variant.
- Encoding many categorical values into bounded indices: hashing can do this, provided possible collisions are acceptable for the use case.
Implementation and data affect the cost of these choices. In particular, the cited KD-tree guidance supports a dimensionality caveat, not a universal complexity guarantee for all trees or workloads. No one item on this list is a prerequisite for every machine-learning project.
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