Machine Learning Models: Types, How They Work and How to Choose

Ask someone to name a machine learning model today, and they’re likely to mention ChatGPT or another large language model (LLM). Yet most organizations still rely on many other kinds of models every day — supporting forecasts, risk scores, recommendations, anomaly detection and other bounded predictions. A single enterprise may run dozens or hundreds of models built on entirely different mathematical foundations.

One may be a linear regression with a handful of coefficients, another a gradient-boosted ensemble trained on millions of records, and another a foundation model containing billions of parameters. Different model families make different assumptions about the data, learn different kinds of relationships and introduce different requirements for training, inference and interpretation.

Those differences aren’t academic. They shape which problems a model can solve, how much data it needs, how its outputs can be evaluated and whether it fits the requirements of the task at hand.

The most powerful model isn’t always the best choice. A smaller or more specialized model may deliver comparable results with lower cost, faster performance or simpler evaluation. As Snowflake’s Kyle Schmaus writes, “The challenge is determining where frontier models are truly necessary and where optimized alternatives can achieve the same outcome.” For this reason, enterprises need the flexibility to choose among model families. In practice, that means managing a portfolio of models selected for different tasks, data types and performance requirements.

A machine learning model is a trained representation of patterns or relationships in data. It accepts an input, applies parameters learned during training and produces an output such as a predicted value, class probability, generated sequence or grouping assignment.

The model is distinct from the algorithm used to create it. The algorithm governs how learning occurs, while the model contains the parameters and structure learned from the data.

A decision tree algorithm, for example, searches for feature values that divide training examples into increasingly useful groups. The fitted tree — including its selected features, split thresholds and leaf values — is the model. Another run of the same algorithm on different data may produce a different tree.

This distinction also separates training from inference. During training, the learning algorithm adjusts the model’s parameters to reduce an error or improve another objective. During inference, the trained model applies those parameters to new data. A model that estimates equipment failure risk might learn from historical sensor readings and maintenance records, then run inference each hour as new readings arrive.

Parameters take different forms across model families. In linear regression, they include the coefficient assigned to each input feature. A tree stores split rules and prediction values. A neural network may contain millions or billions of weights distributed across layers. Whatever their structure, these learned values constitute much of what the model retains from training.

A production model typically includes more than just learned parameters. It may also package preprocessing logic, expected input schemas, class labels, dependency information and metadata needed to reproduce predictions reliably.

Watch Coinbase’s Tuhin Ghosh share how Snowflake ML capabilities are simplifying the way Coinbase delivers machine learning at scale:

A typical machine learning workflow begins with a data set containing observations relevant to the task. Each observation is represented through features, the input variables available to the model. For supervised learning, the data also includes a label or target value that represents the desired output. A credit-risk data set, for example, might contain features describing income, payment history and existing debt, along with a label indicating whether an earlier borrower repaid a loan.

Before training begins, practitioners usually clean and transform the data, construct useful features and divide the available examples into training, validation and test sets. These partitions serve different purposes: the training set fits the model, the validation set supports model and hyperparameter selection, and the test set provides a final estimate using data that didn’t guide development.

The training algorithm then passes examples through the model and compares its output with the desired result. A loss function converts the difference into a numerical value. Optimization adjusts the parameters to reduce that loss, although the exact fitting process differs considerably across model families. A linear model may estimate coefficients through an analytical solution or iterative optimization, while a neural network typically computes gradients and propagates them backward through its layers.

Practitioners also set hyperparameters, which control the learning process or model structure rather than being learned directly from the training examples. These may include the maximum depth of a tree, the number of trees in a random forest, a regularization penalty or the learning rate used to update neural-network weights.

Once fitted, the model is evaluated on held-out data. The relevant metric depends on the task and the consequences of its errors. Accuracy may be useful when classes are reasonably balanced and the costs of different errors are similar, while precision, recall, calibration or the area under a curve may better describe other systems. Regression tasks may use mean absolute error, root mean squared error or a domain-specific cost function. Metric selection is part of the problem definition: two models can change rank when evaluated against different measures.

The goal is generalization — performance on new examples drawn from the conditions the model is expected to encounter. A model can fit its training data closely without learning relationships that transfer beyond it, resulting in poor results in production. That gap is why evaluation must use examples excluded from fitting and why production performance still requires monitoring after deployment.

Model quality is also bounded by the information available in the data. Missing variables, inconsistent measurements, inaccurate labels and unrepresentative samples can teach the model relationships that don’t hold in production. Different algorithms tolerate noise, outliers and missing values differently, but no model can recover a signal that the data never captured.

There isn’t one definitive list of machine learning model types because models can be grouped along several dimensions: by learning approach, by task and by model family.

A random forest, for instance, is a tree-based model family. It generally uses supervised learning and can perform either classification or regression. Keeping those dimensions separate makes it easier to understand how models relate to one another.

By learning approach

Learning approaches differ according to the kind of feedback available during training. Some models learn from labeled examples, while others identify structure in unlabeled data, derive targets from the data itself or improve through rewards received from an environment.

• Supervised learning trains a model from examples containing both inputs and known target values. It supports tasks such as classification, regression and ranking.
• Unsupervised learning works without predefined target labels. The model instead identifies structure in the inputs, perhaps by grouping similar observations, estimating a data distribution or learning a lower-dimensional representation.
• Semi-supervised learning combines a smaller collection of labeled examples with a larger collection of unlabeled data. It’s useful when inputs are plentiful but expert labeling is expensive or slow.
• Self-supervised learning derives training targets from the data itself. A language model may learn to predict missing or subsequent tokens, while an image model may learn by reconstructing masked regions or comparing transformed views of the same image. This approach supports much of the pretraining used for modern foundation models.
• Reinforcement learning trains an agent through interactions with an environment. Rather than learning from a fixed label for each example, the agent receives rewards associated with actions and learns a policy intended to maximize cumulative reward.

Introductory explanations sometimes refer to supervised, unsupervised, semi-supervised and reinforcement learning as the “four types of machine learning.” That remains a useful high-level answer, provided they’re identified as learning approaches rather than four exhaustive types of models.

By task

Models can also be grouped by the kind of output they produce. The same family may support several tasks, including classification, regression, clustering, ranking, forecasting and generation.

• Classification assigns an input to one or more categories. A model might classify a transaction as legitimate or potentially fraudulent, identify an object in an image or route a support request to the correct queue.
• Regression models predict a continuous numerical value, such as demand, temperature or delivery time.
• Clustering groups observations according to a similarity measure without relying on predefined category labels.
• Ranking orders possible results, as in search, recommendations or lead prioritization.
• Forecasting estimates future values while accounting for temporal order and patterns such as seasonality.
• Generation produces new content or structured outputs, including text, images, audio, code and molecular representations.

Other tasks include anomaly detection, dimensionality reduction, representation learning and control. A single model may also support several tasks, particularly when it has been pretrained on broad data.

By model family

A model family describes the structure through which the model represents relationships. These categories can overlap. An autoencoder is both a neural-network architecture and a dimensionality-reduction model, for example, while a transformer is a neural-network architecture used by many foundation models. The family label tells practitioners something about the model’s mechanics, assumptions and computational profile, but it doesn’t fully define the task the model is intended to perform.

The following families cover many of the models used in applied machine learning. They are representative rather than exhaustive, and several include algorithms that support more than one task.

Linear models

Linear models calculate an output from a weighted combination of input features. In linear regression, the output is a continuous value. Logistic regression applies a related formulation to estimate the probability of a class.

Suppose a model estimates delivery time from route length, order volume and warehouse workload. A linear model assigns a coefficient to each feature, representing how the predicted delivery time changes as that feature changes, assuming the others remain fixed.

That structure makes linear models useful baselines. They train quickly, run efficiently during inference and expose relationships that practitioners can inspect. Regularization methods such as ridge, lasso and elastic net constrain the coefficients, which can reduce overfitting and, in some cases, simplify the feature set.

The same structure is also a limitation, however. A basic linear model can’t capture a curved relationship or interaction unless those terms are supplied as features. Where the underlying relationship is highly nonlinear, a linear model may underfit even when it’s configured correctly.

Decision trees

A decision tree represents a prediction as a sequence of feature-based splits. At each internal node, the model evaluates a condition such as whether an account balance exceeds a learned threshold. The path through those conditions leads to a leaf containing a predicted class, probability or numerical value.

Trees can represent nonlinear relationships and interactions without requiring practitioners to specify them in advance. They can also work with a mix of feature types and generally require less scaling or normalization than distance-based and linear methods.

A small tree is relatively easy to inspect because a prediction can be traced through a finite set of rules, but interpretability declines as the tree grows deeper and develops many branches. Individual trees can also be unstable. A modest change in the training data may alter an early split and produce a substantially different structure. Constraining depth, requiring more examples per leaf or pruning the fitted tree can reduce overfitting, although these controls may also prevent the model from representing useful complexity.

Ensemble tree models

Ensemble methods combine several models so their aggregated prediction is more accurate or stable than the result from one component model. Two of the most common tree-based approaches are random forests and gradient boosting.

A random forest fits many decision trees using different samples of the observations and random subsets of candidate features. For classification, the forest combines the trees’ predictions, either by taking a majority vote or by averaging their predicted class probabilities. For regression, their numerical predictions are averaged. Because the trees don’t all make the same errors, aggregation typically reduces the variance associated with one fitted tree.

Gradient-boosted trees are built sequentially. Each new tree focuses on improving the errors left by the current ensemble. Implementations such as XGBoost, LightGBM and CatBoost add different optimizations and controls, but all use boosting rather than the more independent tree construction found in a random forest.

Tree ensembles frequently perform well on structured tabular data, where observations appear as rows and features as columns. Their trade-offs include larger model artifacts, additional training or inference cost and reduced direct interpretability compared with a small tree. Feature-importance measures and post hoc explanation methods can surface aspects of their behavior, but they don’t turn the ensemble into a simple set of rules.

Support vector machines

A support vector machine (SVM) learns a boundary that separates classes while maximizing the margin between that boundary and the closest training examples. Those nearby examples, known as support vectors, determine the fitted decision boundary.

When the classes can’t be separated by a straight line or flat hyperplane, a kernel function can represent nonlinear relationships through similarities between observations. Support vector regression applies related mechanics to continuous prediction.

SVMs can perform well on moderate-size, high-dimensional data, particularly where the features already provide a useful representation. Text classification based on sparse word or token features is a traditional example.

The method becomes less practical as the number of training examples grows, especially with nonlinear kernels. Results also depend on feature scaling and on choices such as the kernel, regularization strength and kernel parameters. Because the learned boundary is defined through support vectors rather than a short coefficient list or decision path, explaining an individual prediction can require additional analysis.

Nearest-neighbor models

Nearest-neighbor methods predict from the training examples most similar to a new input. A k-nearest neighbors classifier identifies a set number of the most similar training examples and assigns a class based on their votes. For regression, it can average their target values.

This is sometimes described as a lazy-learning approach because fitting involves relatively little parameter estimation. Instead, the model retains the reference examples and performs much of its work during inference.

The method is easy to understand and can represent irregular decision boundaries, but its behavior depends on the definition of distance. Features measured on very different scales can distort similarity unless they’re standardized, and irrelevant dimensions can make genuinely similar examples appear far apart.

Inference may also become expensive as the reference data grows. Indexing and approximate-neighbor techniques reduce the search cost, but nearest-neighbor models still place different storage and latency demands on a system than a compact linear model or decision tree.

Probabilistic models

Probabilistic models represent relationships through probability distributions. Rather than producing only a class or numerical estimate, they may calculate the probability of several outcomes or represent uncertainty in hidden variables.

Naive Bayes classifiers estimate a class from the conditional probability of observed features, using an independence assumption that makes fitting efficient. Gaussian mixture models describe data as a combination of probability distributions and are often used for clustering or density estimation. Bayesian networks represent conditional relationships among variables, while hidden Markov models represent sequences whose underlying states aren’t directly observed.

These models are useful when uncertainty is part of the problem rather than an incidental output. A probabilistic forecast, for example, can estimate a distribution of possible demand instead of one point value.

Their assumptions can make them computationally efficient and interpretable, but performance depends on whether those assumptions reasonably describe the data. A simplified dependency structure may fail to capture important relationships, while a more expressive probabilistic model can become difficult to estimate.

Clustering models

Clustering models find groups of similar observations without training against known class labels. Because no single definition of a cluster fits every data set, different methods look for different kinds of structure.

K-means assigns observations to a specified number of clusters, each represented by a learned centroid. It works best when distance from a centroid meaningfully represents membership and the clusters have broadly compatible shapes and scales.

Hierarchical clustering repeatedly combines or divides groups to create a nested structure. Practitioners can inspect that hierarchy at different levels rather than committing to one flat grouping at the start.

Density-based methods, including DBSCAN, identify regions containing closely packed observations and can mark isolated points as noise. This allows them to find cluster shapes that centroid-based methods may miss, although the result depends on density thresholds.

Clusters don’t have inherent business meaning. After fitting, practitioners need to inspect the features and examples associated with each group, test whether the pattern is stable and decide whether it supports the intended analysis or workflow.

Dimensionality-reduction models

Dimensionality reduction represents data using fewer variables while preserving selected aspects of its structure. The resulting representation can support visualization, denoising, compression or downstream model training.

Principal component analysis (PCA) learns new axes that capture progressively smaller amounts of variation in the input. Because the components are weighted combinations of the original features, PCA can condense correlated measurements into a smaller feature space.

Other methods preserve different properties. Some emphasize local neighborhoods for visualization, while autoencoders use a neural network to compress an input into a lower-dimensional representation and reconstruct it.

Reducing the number of features can lower storage and computation costs or remove noise, but it can also discard information. The learned components may be harder to explain than the original variables, and a representation that preserves overall variance may not preserve the distinctions most important to the prediction task.

Neural networks

A neural network consists of layers that transform an input through learned weights and nonlinear functions. During training, the network produces an output, computes a loss and propagates gradients backward so an optimizer can update its parameters.

Different architectures impose structures suited to different data:

• Feedforward networks pass information through a sequence of layers and support general classification and regression tasks.
• Convolutional neural networks apply learned filters across spatial inputs and are commonly used for images.
• Recurrent neural networks maintain state across sequences, although transformers now handle many tasks once assigned to recurrent architectures.
• Transformers use attention mechanisms to model relationships among elements in a sequence or other tokenized representation.
• Autoencoders learn compressed representations by reconstructing their inputs.
• Graph neural networks exchange information among connected nodes to model relational structures.

Neural networks can learn complex functions and, with enough data, derive useful representations from images, audio, text and other high-dimensional inputs. That flexibility often comes with higher data and compute requirements, more extensive tuning and less direct interpretability than simpler models.

Architecture alone doesn’t determine quality. Data construction, objective design, optimization, regularization and evaluation all influence whether a neural network generalizes beyond its training examples.

Time-series models

Time-series models account for the order in which observations occur. A forecasting system may need to represent trends, recurring seasonal patterns, delayed effects and external variables that change over time.

Statistical models such as autoregressive integrated moving average use past values and residual patterns to estimate future observations. Exponential-smoothing methods model components such as level, trend and seasonality. Tree ensembles can use engineered lag and rolling-window features, while recurrent networks and transformers can learn temporal representations from sequences.

Time-series model selection often comes down to balancing accuracy, interpretability and computational cost. Schmaus explains, “Classical methods, such as ARIMA and Exponential Smoothing, are fast to train, highly efficient and easy to interpret, while neural methods can have increased accuracy and can incorporate exogenous inputs and non-linearities, though at the expense of training and inference efficiency.”

Evaluation must also preserve temporal order. A random train-test split may leak future information into training and produce an unrealistically favorable result. Practitioners instead use time-based holdouts or rolling evaluation windows that more closely resemble how forecasts will be generated.

Foundation models

A foundation model is trained on broad data, typically at substantial scale, and designed to support adaptation across many downstream tasks. The category includes LLMs as well as models trained for images, audio, biological sequences and other modalities.

Most foundation models use neural networks and self-supervised pretraining. Instead of collecting a separate human-authored label for each example, the training process derives objectives from the data, such as predicting a missing token or aligning related image and text representations.

After pretraining, the model may be adapted through prompting, retrieval, fine-tuning or additional task-specific layers. This reuse distinguishes it from a model trained solely for one bounded prediction task.

Examples include LLMs such as GPT, Claude, Llama and Mistral, along with multimodal models such as Gemini and Qwen2.5-VL. The category also includes image-generation models such as Stable Diffusion and FLUX, speech models such as Whisper and biological foundation models such as ESM. Although these models support very different tasks, they share the characteristic of broad pretraining followed by adaptation to downstream applications through prompting, retrieval, fine-tuning or additional training.

Model selection is an empirical process. Different families make different assumptions about the data and introduce different trade-offs, so practitioners typically compare several candidates before selecting the one that best fits the task and operating requirements.

Define the task and evaluation criteria

First determine whether the output is a class, numerical value, ranking, cluster, forecast or generated result. Then identify how the output will be used.

In a medical screening system, for example, missing a positive case carries a very different cost than sending an additional case for review. In demand forecasting, an underestimate may be more expensive than an equally large overestimate. The evaluation metric should represent those consequences rather than merely being conventional for the task.

Examine the data available

Sample size, feature types, sparsity, label quality and temporal structure narrow the practical candidates.

Tree ensembles often provide strong baselines for structured data containing mixed feature relationships. Linear models work well where the relationship is approximately linear or transparency is important. SVMs may suit moderate-size, high-dimensional data, while neural networks are often considered when large quantities of unstructured or high-dimensional data are available.

Those are starting points rather than selection rules, however. In a benchmark spanning 176 tabular data sets, the relative performance of neural networks and gradient-boosted trees varied with characteristics such as feature distributions, and modest tuning sometimes mattered more than the choice between the two families.

Set operational constraints early

A model that produces the best offline metric may still be unsuitable for its production setting. Teams may need to limit:

• Training time and infrastructure cost
• Inference latency
• Memory and model size
• Frequency and cost of retraining
• Hardware dependencies
• Explanation requirements
• Volume and pattern of prediction requests

Operational trade-offs often change which model is the better choice. A nearest-neighbor model may require little training but shift more of the cost to inference, while an ensemble can improve predictive performance at the expense of latency and memory. A simpler linear model may therefore be easier to audit, deploy and retrain, even when a more complex candidate produces a modestly better score.

Establish a simple baseline

A baseline shows whether additional complexity produces a meaningful gain. For regression, that may begin with predicting the historical average and then fitting a linear model. For classification, practitioners might compare a majority-class rule, logistic regression and a shallow decision tree before adding ensembles or neural networks.

The baseline also tests the pipeline. Unexpectedly strong results may reveal leakage, while unexpectedly weak results can expose problems in labels, joins, feature construction or evaluation.

Compare models under consistent conditions

Candidate models should use the same data partitions and evaluation procedure. Hyperparameter tuning needs to occur without consulting the final test set, since repeated decisions based on test performance turn that set into another source of training feedback.

Cross-validation can estimate how performance varies across different subsets of the data, particularly when the available sample is limited. Time-dependent data requires splits that preserve chronology.

Model comparison should also include variability. A slightly higher average score may not be meaningful when results change widely across folds, seeds or time windows.

Balance underfitting and overfitting

An underfit model is too constrained to capture important relationships. An overfit model learns patterns specific to its training sample and performs less reliably on new data.

This is often described through the bias-variance trade-off. High-bias estimators tend to make systematic simplifications, while high-variance estimators respond strongly to changes in the training set. Model family, hyperparameters, regularization and training-data volume all affect that balance.

The objective isn’t to select the most flexible model, but rather to find enough flexibility to represent the useful signal without fitting noise or creating operational costs that the gain cannot justify.

Test under expected production conditions

Before deployment, evaluation should include conditions the model is likely to encounter: missing fields, new categories, delayed features, unusual volumes and shifts in the input distribution.

The serving path deserves testing as well. Preprocessing applied during training must be reproduced during inference, feature definitions must remain consistent and latency should be measured across the complete request path rather than the model calculation alone.

Selection remains provisional after launch. Once the model is exposed to production data, monitoring can show whether inputs, predictions and outcomes still resemble the conditions under which it was evaluated.

A machine learning model encodes patterns in its training data, It doesn’t independently determine whether those patterns are complete, causal or appropriate for the decision being made.

Incomplete representation

Training data is a sample of past observations produced by existing systems and processes. It may omit relevant populations, rare conditions or variables that weren’t recorded. A model trained from that sample can perform well on a held-out subset while remaining unreliable in settings that the source data barely represented.

Overfitting and distribution shift

Even a carefully evaluated model may encounter production data that differs from its training data. Customer behavior changes, sensors are replaced, upstream definitions are revised and new products alter the feature distribution. Some shifts reduce overall performance, while others affect only a particular population or operating condition. For this reason, aggregate monitoring can miss localized degradation.

Bias in data and labels

Historical labels reflect how earlier decisions were made and measured. If some groups received fewer opportunities, less complete follow-up or different review standards, the resulting label may encode those differences.

Data bias can also enter through sampling, missing values, proxy variables and the choice of optimization target. Mitigation requires examining how the data was produced, evaluating results across relevant groups and reviewing how the model’s outputs influence later data collection.

Limited interpretability

Models vary in how easily their mechanics can be inspected. Linear coefficients and small decision trees provide relatively direct representations, while large ensembles and neural networks distribute a prediction across many interacting components.

Post hoc explanation methods can estimate feature influence or characterize local behavior, but those explanations have their own assumptions and limitations. The required level of interpretability depends on the decision, audience and applicable governance requirements.

Correlation without causation

Predictive models often exploit correlations that improve estimates without identifying the mechanism that produced them. A feature may be highly predictive because it serves as a proxy for another variable or because an earlier business process created the relationship.

This can be sufficient for some forecasting tasks, but it’s less so when the organization wants to know what intervention will change the outcome. Causal questions require study designs and assumptions beyond standard predictive modeling.

Maintenance costs

A model is connected to data pipelines, feature logic, serving infrastructure and business definitions. Changes in any of those components can affect its behavior. Teams need to retain training context, version model artifacts, monitor production behavior and determine when to investigate, retrain or retire a model. The operational burden grows when an organization maintains many models with different owners, dependencies and refresh schedules.

Model development draws on data preparation, experimentation, compute, version management and inference. When those stages are split among disconnected environments, teams must reproduce data sets and feature logic, move artifacts between systems and reconstruct which inputs produced a particular model version.

Snowflake ML supports these stages alongside governed enterprise data. Practitioners can prepare features, train models, compare experiments, register model versions, run inference and monitor production behavior through Snowflake capabilities.

Snowpark provides DataFrame APIs for querying and processing data in Snowflake with Python, Java or Scala. For Python machine learning workflows, practitioners can construct transformations and work with Snowflake data without first exporting the complete training set to a separate processing system.

For workloads that require specialized libraries, CPUs or GPUs, Snowflake Container Runtime provides preconfigured, customizable environments for experimentation, model training, hyperparameter tuning and batch inference. The environments are versioned so teams can pin a workload to a specific runtime and plan migrations to later versions.

Once trained, a model can be logged in Snowflake Model Registry with its metadata and versions. The registry supports common model types including scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, TensorFlow, Keras, MLflow models and Hugging Face pipelines, with support for custom model types through the CustomModel interface.

Registered models can run batch inference in a Snowflake virtual warehouse or use Snowpark Container Services for workloads that require other compute characteristics. Snowflake also supports managed real-time inference services with dedicated HTTP endpoints for interactive, low-latency applications.

Keeping model versions and metadata within the governed environment also gives teams a clearer record of what is serving predictions. Access controls can determine who manages or invokes a model, while stored metrics and metadata support comparison and lifecycle management. The model remains one part of the production system, but its relationship to data, compute and governance is easier to preserve when those components operate through a shared platform.

Machine learning models differ in how they represent relationships, what data they work well with and what trade-offs they introduce. That diversity is part of how enterprise machine learning develops in practice. Organizations need the flexibility to evaluate different families, select the model that fits each task and support a portfolio that can evolve as data, requirements and available methods change.