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title: What is AI classification? Techniques, types, and examples
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# What is AI classification? Techniques, types, and examples

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# What is AI classification? Techniques, types, and examples

AI classification assigns inputs to predefined classes. How it works, binary vs multiclass vs multilabel, the main algorithms, and how to evaluate a classifier.

Editor: [Emily Bowen](https://www.linkedin.com/in/emilywynne/)

**Updated September 2026**

AI classification is a machine learning task in which a model assigns each input to one of a fixed set of categories, called classes. The model learns from labeled examples, a method called supervised learning. It then labels inputs it has never seen: an email as spam or not spam, a photo as a cat or a dog, a call as answered by a person or a machine.

That last example runs on live phone lines. When an outbound call is answered, answering machine detection classifies it in real time as a human or a machine, and the call flow branches on the result. The label is only the visible half of any classifier. Behind it sit a score, a threshold someone chose, and a rate of mistakes in each direction, and those three decide whether the classifier is worth using.

## What is AI classification?

AI classification is prediction with a fixed answer set: the output is always drawn from classes fixed before prediction, one class or, in multilabel tasks, several, and never a number on a continuous scale. The trained model is called a classifier. Its classes usually come from the training labels, so a spam filter trained on "spam" and "not spam" can answer only those two, however strange the email. A prompted language model takes its classes from the prompt instead.

A classifier learns from many labeled instances, not from rules someone wrote down. The labels are the  the model is trained and measured against, and producing them is the work of . A classifier that reads human language learns the same way as one that reads images: it sees a large set of examples paired with their correct class and adjusts until its answers match.

This content was generated with the assistance of AI. Our AI [prompt chain](https://telnyx.com/learn-ai/promptchain-ai) workflow is carefully [grounded](https://telnyx.com/learn-ai/grounding-ai) and preferences .gov and .edu citations when available. **All content is reviewed by a Telnyx employee to ensure accuracy, relevance, and a high standard of quality.**

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ground truth

[data labeling](https://telnyx.com/learn-ai/what-is-data-labeling)

That learning task is only one meaning of "AI classification"; the phrase has two others. Classifying AI systems sorts the systems themselves, by capability or by risk, as the EU AI Act does with its risk-based rules. Data classification labels information by sensitivity, such as the low, moderate, and high impact levels in NIST's [FIPS 199](https://csrc.nist.gov/pubs/fips/199/final) standard. Data classification is often done with the machine learning task itself; classifying AI systems is a policy and regulatory use of the word.

## How does AI classification work?

AI classification works in two phases: training, where the model learns a scoring rule from labeled examples, and prediction, where it scores a new input and a threshold turns the score into a label. Logistic regression, a simple and widely used classifier, shows both phases with numbers small enough to follow. Other algorithms learn different scoring rules, but most end the same way, with a score and a cutoff.

Training starts from features, the measurable properties the model can see. For an email, three useful features are the number of links to unfamiliar domains, whether the subject line contains words such as "urgent" or "verify," and whether the sender is in the recipient's contacts. Training adjusts one weight per feature, plus a starting value, until the model's scores agree as closely as possible with a set of emails people have already labeled. A [loss function](https://telnyx.com/learn-ai/loss-function) measures the disagreement; for logistic regression it is cross-entropy, also called log loss, which grows fast when the model is confident and wrong. [Gradient descent](https://telnyx.com/learn-ai/batch-gradient-descent) then moves each weight a small step in the direction that lowers the loss, over many passes through the labeled emails.

Suppose training settles on a starting score of -2.0 and three weights: plus 0.8 per unfamiliar link, plus 1.6 for an urgent subject, and minus 2.5 for a known sender. A new email with three unfamiliar links, an urgent subject, and an unknown sender scores -2.0 + 2.4 + 1.6 = 2.0\. The logistic function, also called the sigmoid, maps any score into the range 0 to 1, and 2.0 becomes 0.88\. With the threshold at 0.5, the email is labeled spam.

A second email, with one unfamiliar link, a normal subject, and a known sender, scores -2.0 + 0.8 - 2.5 = -3.7\. The sigmoid maps that to 0.02, so the email is labeled not spam. The raw score before the sigmoid is called a [logit](https://telnyx.com/learn-ai/logits-ai), or the log-odds, and the number after it is the model's estimated probability of spam.

The threshold is a choice, not part of the learned model. [scikit-learn](https://scikit-learn.org/stable/modules/linear%5Fmodel.html#logistic-regression) applies 0.5 by default, and moving it trades missed spam against real mail sent to the spam folder. Running the trained model on each new email is called [inference](https://telnyx.com/resources/machine-learning-inference), and it is the step that runs on every message, call, or transaction a production classifier sees.

The spam filter's three features are hand-picked counts and flags, and most raw data has to become numbers before a classifier can use it. Text is often turned into word counts, called a bag of words, and weighted by TF-IDF so that common words such as "the" count for less, as the [scikit-learn text guide](https://scikit-learn.org/stable/modules/feature%5Fextraction.html#text-feature-extraction) describes. Images arrive as pixel values. [Embeddings](https://telnyx.com/learn-ai/embedding-layer) map text, audio, or images to vectors that place similar inputs close together. Neural networks and large language models learn their own features from those raw forms.

## Binary, multiclass, and multilabel classification

Classification problems come in three shapes, set by how many labels each input can take. Binary classification picks one of two classes, multiclass picks one of three or more, and multilabel assigns any number of labels to one input. The shape decides what the model's final step produces and how its mistakes are counted.

Standard [answering machine detection](https://developers.telnyx.com/docs/voice/programmable-voice/answering-machine-detection) on the Telnyx [Voice API](https://telnyx.com/products/voice-api) is a binary classifier with a fallback answer. It returns `human`, `machine`, or `not_sure`, and the docs recommend treating `not_sure` "as if human answered." `not_sure` works as an abstain option: the classifier declines to pick a side when the audio does not settle the question.

Routing `not_sure` to the human path is a threshold decision stated in plain language. When the classifier cannot tell, the call takes the path where a mistake does less harm. Treating a person like a voicemail box costs more than leaving a message on a machine. Telnyx does not publish accuracy figures for answering machine detection, so its error rates are best measured on the calls it will actually handle.

Premium answering machine detection is multiclass: each answered call gets exactly one of several classes. It returns `human_residence`, `human_business`, `machine`, `silence`, or `fax_detected`, or `not_sure` when it cannot decide.

A neural network, covered with the algorithms below, produces its answer in its last layer, the output layer, and for multiclass tasks that layer is usually a softmax. Softmax turns one score per class into probabilities that sum to 1 ([Deep Learning](https://www.deeplearningbook.org/contents/mlp.html), section 6.2.2.3). Algorithms built for two classes handle more through one-vs-rest, which fits one classifier per class against all the others, and which scikit-learn's [multiclass guide](https://scikit-learn.org/stable/modules/multiclass.html) calls "a fair default choice."

Multilabel classification drops the one-class limit: each input can take any number of labels from the set, zero included. A support call transcript can be about billing and a cancellation at the same time, so a two-label answer is correct there. Softmax is the wrong output for that job, because it forces the classes to compete for one share of probability. A multilabel model instead gives each label its own sigmoid and its own threshold, and the simplest version trains one binary classifier per label.

Lists of classification types often add a fourth, imbalanced classification, where one class is rare, such as fraud among card transactions. Imbalance is a property of the data, and a fraud model is still binary. It changes how the classifier is trained and scored, which is why accuracy alone misleads on imbalanced data.

## Classification vs regression vs clustering

Classification predicts a category, regression predicts a number, and clustering finds groups in data that has no labels. The first two are supervised learning, because both train on examples with known answers; clustering is unsupervised.

The split between classification and regression comes from the kind of answer the model gives. In the terms of James, Witten, Hastie, and Tibshirani's [statistical learning textbook](https://www.statlearning.com/), a qualitative response makes a classification problem and a quantitative one makes a regression problem. The same email inbox supports both: "is this spam?" is classification, and "how many hours until this email gets a reply?" is regression. Several algorithms, including k-nearest neighbors and gradient boosting, come in both versions.

Clustering differs from both classification and regression because it needs no labels at all. A clustering algorithm, a form of [unsupervised learning](https://telnyx.com/learn-ai/unsupervised-learning-ai), groups emails that look alike, such as receipts, newsletters, and team threads, without being told those groups exist. The groups have no names until a person looks at them. Once someone names them, those names can become labels, and a classifier can learn to assign them.

|                        | Classification                | Regression                     | Clustering                  |
| ---------------------- | ----------------------------- | ------------------------------ | --------------------------- |
| Output                 | One of a fixed set of classes | A number on a continuous scale | A group, found in the data  |
| Labels needed to train | Yes, a class per example      | Yes, a number per example      | No                          |
| Learning type          | Supervised                    | Supervised                     | Unsupervised                |
| Email example          | Spam or not spam              | Hours until a reply            | Topics in an untagged inbox |
| Typical metrics        | Precision, recall, F1         | Mean absolute error            | Silhouette coefficient      |

## Types of AI classification algorithms

The standard AI classification algorithms are logistic regression, decision trees and random forests, support vector machines, k-nearest neighbors, naive Bayes, gradient boosting, and neural networks. Large language models now join them for text, classifying with no task-specific training. Each one draws the boundary between classes in its own way, which is why each suits different data.

### Logistic regression

Logistic regression is a linear classifier: it adds weighted features into one score and passes that score through the sigmoid to get a class probability, exactly as in the spam example. Despite the name, [scikit-learn](https://scikit-learn.org/stable/modules/linear%5Fmodel.html#logistic-regression) implements it "as a linear model for classification rather than regression."

A linear classifier separates classes with a straight line, or in more dimensions a flat plane called a hyperplane. That makes it fast to train and easy to inspect, since each weight shows how far a feature pushes the score, and it is the usual baseline other models must beat. It cannot draw a curved boundary unless someone adds features that describe the curve.

### Decision trees and random forests

A decision tree classifies by asking a sequence of yes-or-no questions about the features, such as "more than two unfamiliar links?", and each leaf at the end of a path holds a class. Training picks each question to make the resulting groups as pure as possible, measured by Gini impurity or [entropy](https://telnyx.com/learn-ai/entropy-machine-learning), as the [scikit-learn tree guide](https://scikit-learn.org/stable/modules/tree.html#tree-algorithms-id3-c4-5-c5-0-and-cart) describes. Trees are easy to read, and a single deep tree also memorizes its training data easily.

A random forest trains many trees, each on a bootstrap sample, a random sample of the training data drawn with replacement, and each choosing among a random subset of features at every split, then combines them. [Breiman's 2001 paper](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) combines the trees by majority vote; scikit-learn's version averages their predicted probabilities. The two sources of randomness lower the variance of the forest, meaning how much its predictions would change if it were trained on a different sample. That is why a forest usually generalizes better than any one of its trees.

### Support vector machines (SVM)

Support vector machines (SVM) are powerful classifiers that work by finding the hyperplane that best separates different classes in the feature space. "Best" means the widest margin, the largest gap to the nearest training points on either side. Those nearest points are the support vectors that give the method its name ([scikit-learn](https://scikit-learn.org/stable/modules/svm.html#mathematical-formulation)).

In 1995, [Cortes and Vapnik](https://link.springer.com/article/10.1007/BF00994018) extended the maximum-margin method to data that no hyperplane separates cleanly, by allowing some points inside the margin at a cost. SVMs also handle non-linear boundaries. For a curved boundary, such as a ring around one class, a kernel function compares points as if they had been mapped into more dimensions, without computing that mapping. The cost is training time: scikit-learn notes that the fit time of its [SVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) "scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of samples."

### K-nearest neighbors (KNN)

K-nearest neighbors (KNN) classifies a new point by "the majority vote of the nearest k neighbors," in the words of [Cover and Hart](https://ieeexplore.ieee.org/document/1053964). With k set to 5, three spam emails among the five nearest make the new email spam.

No model is learned. Fitting only stores the training data, sometimes in a search index such as a KD tree or ball tree ([scikit-learn](https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-classification)). Most of the computation happens at prediction time, when each new point searches for its neighbors. KNN is simple to explain, but it slows as the dataset grows and weakens as features multiply, because distances carry less meaning in many dimensions, the problem known as the [curse of dimensionality](https://telnyx.com/learn-ai/curse-of-dimensionality).

### Naive Bayes

Naive Bayes applies [Bayes' theorem](https://telnyx.com/learn-ai/bayesian-machine-learning-ai) to compute the probability of each class given the features, under the naive assumption that the features are independent of each other once the class is known. The assumption is usually false, since spam words tend to appear together, yet naive Bayes classifiers "have worked quite well in many real-world situations, famously document classification and spam filtering," per the [scikit-learn guide](https://scikit-learn.org/stable/modules/naive%5Fbayes.html). They train in a single pass over the data.

Spam filtering is where naive Bayes made its name: [Sahami and colleagues](https://cdn.aaai.org/Workshops/1998/WS-98-05/WS98-05-009.pdf) applied a Bayesian classifier to junk email in 1998, early work in the statistical filters that followed.

### Gradient boosting

[Gradient boosting](https://telnyx.com/learn-ai/gradient-boosting-machines) builds decision trees one after another, and the trees combined so far form the ensemble. Each new tree is fit to the gradient of the loss on the ensemble's current predictions, which for squared error is simply the residuals, the gaps between predictions and true values. Its contribution is scaled down by a learning rate, a regularization called shrinkage, so many small corrections add up. The method was set out by [Friedman in 2001](https://projecteuclid.org/journals/annals-of-statistics/volume-29/issue-5/Greedy-function-approximation-A-gradient-boostingmachine/10.1214/aos/1013203451.full). [XGBoost](https://arxiv.org/abs/1603.02754), published in 2016, made tree boosting scale to very large datasets. On tabular data, tree ensembles remain hard to beat. Across 45 datasets, [Grinsztajn and colleagues](https://arxiv.org/abs/2207.08815) found that tree-based models such as XGBoost and random forests "remain state-of-the-art on medium-sized data," about 10,000 samples, ahead of deep learning.

### Neural networks and deep learning

Neural networks learn their own features. Stacked layers turn the raw input, step by step, into features that separate the classes. The final layer turns the last scores into class probabilities: a [softmax](https://telnyx.com/learn-ai/activation-functions) for multiclass output, a sigmoid for binary and multilabel output. Training minimizes cross-entropy loss, the same measure logistic regression minimizes.

Convolutional neural networks (CNNs) are the neural networks built for image classification. ImageNet results use two measures: top-1 accuracy counts how often the model's single best guess is right, and top-5 error counts how often the right class is missing from its five best guesses. In the 2012 ImageNet challenge, Krizhevsky, Sutskever, and Hinton's [AlexNet](https://papers.nips.cc/paper%5Ffiles/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf) reached a top-5 error rate of 15.3%, against 26.2% for the second-best entry. By 2019, [EfficientNet-B7](https://arxiv.org/abs/1905.11946) reached 84.3% top-1 accuracy. The two figures use different measures, so they cannot be compared directly.

### Large language models and zero-shot classification

Large language models classify text with no task-specific training. In [zero-shot learning](https://telnyx.com/learn-ai/zero-shot-learning), the prompt lists the classes and the model picks one; in [few-shot prompting](https://telnyx.com/learn-ai/few-shot-prompting), the prompt also carries a few labeled examples. The [GPT-3 paper](https://arxiv.org/abs/2005.14165) calls this in-context learning, applied "without any gradient updates or fine-tuning." Before that, [Yin and colleagues](https://arxiv.org/abs/1909.00161) treated zero-shot text classification as an entailment problem, asking whether a text implies the description of each label. The idea also works for images: [CLIP](https://arxiv.org/abs/2103.00020) reached 76.2% top-1 accuracy on ImageNet zero-shot, without training on ImageNet's labeled images.

A prompted LLM is the fast path when labeled examples are scarce or the class list changes often, since changing a class means editing a prompt, not collecting data and retraining. A trained classifier tends to win at high volume. A logistic regression or a tree ensemble costs far less per input than an LLM call and returns the same label for the same input every time. Its score can also be thresholded and calibrated. Telnyx's guide to choosing an [LLM API](https://telnyx.com/resources/llm-api) recommends routing classification to smaller models and keeping the largest for harder requests.

An LLM classifier also needs its output held to the label set. Telnyx's OpenAI-compatible chat completions endpoint takes a JSON schema through `response_format`. When the schema's field is an enum of two values, the model "can only answer with one of `positive` or `negative`," per the Telnyx [JSON mode guide](https://developers.telnyx.com/docs/inference/json-mode).

### How to choose a classification algorithm

The right classification algorithm depends first on the data. For tabular data, such as transactions or account records, gradient boosting or a random forest is the strong default, with logistic regression as the baseline to beat. For images and audio, neural networks lead. An SVM suits small and medium datasets with many features. For text with few labels, a prompted LLM is the quickest start; with many labels and high volume, a trained classifier is cheaper to run. When every decision needs a reason a person can read, logistic regression or a small decision tree keeps the model [explainable](https://telnyx.com/learn-ai/explainable-ai).

## How to evaluate a classifier

A classifier is evaluated by comparing its labels with the true labels on data it did not train on, counted in a confusion matrix of true positives, false positives, false negatives, and true negatives. Every threshold-based metric, such as accuracy, precision, and recall, is a ratio of those four counts ([scikit-learn metrics guide](https://scikit-learn.org/stable/modules/model%5Fevaluation.html)).

The data a classifier did not train on comes from splitting labeled data three ways. A training set fits the model, a validation set tunes choices such as the threshold or a tree's depth, and a test set is held out until the end and scored once ([scikit-learn](https://scikit-learn.org/stable/modules/cross%5Fvalidation.html)). K-fold cross-validation can replace the fixed validation set: it splits the training data into k parts and trains k times, each time scoring on the part left out. Tuning against the test set makes its score look better than the model will perform on new data.

Suppose the three-feature spam filter runs on 1,000 test emails, 100 of them spam. At the 0.5 threshold it flags 110 emails: 80 are spam, the true positives, and 30 are real mail, the false positives. It misses the other 20 spam emails, the false negatives, and correctly passes 870, the true negatives. The four standard metrics follow from those counts:

* Accuracy is correct labels over all labels: (80 + 870) / 1,000 = 95%.
* Precision is true positives over everything flagged: 80 / 110 = 0.73, so 73% of the flagged emails were spam.
* Recall is true positives over all real positives: 80 / 100 = 0.80, so the filter caught 80% of the spam.
* [F1](https://telnyx.com/learn-ai/calculating-f1-score) is the harmonic mean of precision and recall, 0.76 here.

The 95% accuracy says little on its own. A filter that flags nothing scores 90% accuracy on the same emails, because 900 of the 1,000 are real mail, while catching no spam at all.

Precision and recall also move with the threshold. At 0.8, the filter flags 65 emails: 60 are spam and 5 are real mail, so it misses 40 spam emails and correctly passes 895\. Precision rises to 0.92, recall falls to 0.60, F1 falls to 0.73, and accuracy barely moves, from 95% to 95.5%.

Neither threshold is right in general, because the right one depends on which mistake costs more. A spam filter that buries a real invoice does more damage than one that lets an ad through, which argues for the higher threshold. A fraud screen that misses a stolen card argues for a lower one, and the [F2 score](https://telnyx.com/learn-ai/f2-score) weights recall for exactly that case. scikit-learn's [TunedThresholdClassifierCV](https://scikit-learn.org/stable/modules/generated/sklearn.model%5Fselection.TunedThresholdClassifierCV.html), added in version 1.5, searches for the threshold that maximizes a chosen metric.

Curves summarize every threshold at once. The ROC curve plots recall, also called the true positive rate, against the false positive rate, which is false positives over all real negatives (30 / 900 at the 0.5 threshold). The area under it, ROC AUC, is the probability that the classifier ranks a randomly chosen spam email above a randomly chosen real one, as [Fawcett](https://doi.org/10.1016/j.patrec.2005.10.010) shows.

The precision-recall curve plots precision against recall instead. In practice, ROC AUC is the standard way to compare how well two models rank, and when positives are rare, the precision-recall curve at the threshold the system will use shows the precision users actually experience ([Saito and Rehmsmeier](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118432)). [McDermott and colleagues](https://arxiv.org/abs/2401.06091) caution that the area under the precision-recall curve is not a better model-comparison metric in general.

Both curves depend only on how the scores rank the emails, not on the scores' values, so neither says whether a score of 0.88 means an 88% chance of spam. It does only if the model is [calibrated](https://telnyx.com/learn-ai/logits-confidence), meaning that among all emails scored near 0.88, about 88% really are spam.

Checking calibration takes one plot. A calibration curve, also called a reliability diagram, groups test predictions into bins by score. It plots each bin's average score against the fraction that really were spam, and a calibrated model's points sit on the diagonal ([scikit-learn calibration guide](https://scikit-learn.org/stable/modules/calibration.html)). When they drift off it, a correction fitted on held-out data maps scores to calibrated probabilities, using either a sigmoid curve (Platt scaling) or isotonic regression, the two methods in scikit-learn's calibration tools. Neural networks are the classic case for checking: [Guo and colleagues](https://proceedings.mlr.press/v70/guo17a.html) found modern networks often overconfident in 2017, although [Minderer and colleagues](https://arxiv.org/abs/2106.07998) found in 2021 that the newest models without convolutions are among the best calibrated.

Every metric so far treats spam as the single positive class, while multiclass and multilabel classifiers are scored one class at a time. Each class gets its own precision and recall, counted as that class against the rest, and the per-class numbers are then averaged. Macro-averaging "simply calculates the mean of the binary metrics, giving equal weight to each class," per the [scikit-learn guide](https://scikit-learn.org/stable/modules/model%5Fevaluation.html), while micro-averaging pools every decision first, so frequent classes dominate. Macro-averaging keeps a rare class visible in the total.

## Real-world applications of AI classification

AI classification runs wherever a system has to put each item into a known category quickly and at volume: filtering email, recognizing images, reading sentiment, supporting diagnosis, screening payments, and handling phone calls.

### Spam detection

Spam detection is the classic binary classification task: every incoming email gets one of two labels, spam or not spam, before it reaches the inbox. The model learns from mail that users and operators have already marked, using signals from the text and from the sender, like the link and contact features in the logistic regression example. Spammers change their wording to get past filters, so the training data goes stale and the model has to be retrained on recent mail.

### Image recognition

Image recognition classifies what a picture shows: an object, a face, a scene, or a defect on a production line. ImageNet, whose challenge asks models to sort images into 1,000 classes, became the standard test, and the benchmark figures for AlexNet, EfficientNet, and CLIP are all ImageNet results. Facial recognition, autonomous vehicles, and medical imaging all depend on image classifiers.

### Sentiment analysis

Sentiment analysis classifies text, or speech transcribed to text, as positive, negative, or neutral, which makes it a multiclass task, and some systems add finer classes such as frustrated or satisfied. Businesses run it over reviews, social posts, and support conversations to follow opinion at a scale no team could read. On phone calls, [call sentiment analysis](https://telnyx.com/resources/call-sentiment-analysis) applies the same classifier to the transcript of each conversation, so a contact center can find the calls that went badly without listening to all of them.

### Medical diagnosis

Medical diagnosis uses classifiers to flag disease in images, lab results, and patient records, as decision support for clinicians. The best-known result came from Stanford. [Esteva and colleagues](https://www.nature.com/articles/nature21056) trained a convolutional network on 129,450 clinical images covering 2,032 diseases. Tested against 21 board-certified dermatologists on biopsy-proven images, it performed on par with all the tested experts. A diagnostic classifier is only as good as its labels, which is why that study used biopsy-proven diagnoses as its ground truth.

### Fraud detection

Fraud detection classifies each transaction as legitimate or fraudulent before a payment is approved. It is the standard example of imbalanced classification, because fraudulent transactions are a small fraction of the total. It also shows why the threshold matters: every false positive is a declined customer, and every false negative is a loss.

### Call and voice classification

Phone calls carry some of the most time-sensitive classification problems, because the decision has to land while the call is still live. Answering machine detection sorts answered calls into human or machine. Telnyx's Premium mode, which the docs describe as using "advanced speech recognition technology and machine learning," also separates a person at home from a person at a business and recognizes silence and fax machines. The same first question applies to [voice AI agents](https://telnyx.com/products/voice-ai-agents) placing outbound calls, since an agent that speaks to a voicemail greeting as if it were a person wastes the call.

Telnyx [Deepfake Detection](https://developers.telnyx.com/docs/voice/programmable-voice/deepfake-detection) asks a different question of the same call: whether the caller's voice is human or [AI-generated](https://telnyx.com/resources/detect-ai-generated-voice-live-calls). It returns a score for the probability that the audio is AI-generated and a consistency value, a 0 to 100 percentage for how consistently the model classified the audio across frames. The Telnyx docs advise reading the two together, since a high score with low consistency "may warrant additional verification rather than an immediate hangup": threshold design in one sentence. As with answering machine detection, no accuracy figures are published.

## Challenges in AI classification

The main challenges in AI classification come from the data: rare classes, biased or wrong labels, models that memorize their training set, and inputs that change after training.

### Handling imbalanced datasets

Handling imbalanced datasets means keeping a rare class, such as fraud or a disease, from being ignored by a model that can score well by always predicting the common class. The standard remedies act at different points. [SMOTE](https://www.jair.org/index.php/jair/article/view/10302) creates synthetic examples of the rare class before training, [class weights](https://scikit-learn.org/stable/glossary.html#term-class%5Fweight) make each rare-class mistake cost more during training, and threshold tuning moves the decision point after training. Whichever remedy is used, the result should be judged on precision and recall, not accuracy.

### Bias in training data

Bias in training data becomes bias in the classifier, because the model learns whatever pattern the labels contain, including gaps in who is represented. Buolamwini and Gebru's [Gender Shades](https://proceedings.mlr.press/v81/buolamwini18a.html) study tested commercial gender classification systems and found error rates of up to 34.7% for darker-skinned women, against at most 0.8% for lighter-skinned men. Fixing it starts with measuring error separately for each group. Toolkits such as [AI Fairness 360](https://github.com/Trusted-AI/AIF360) bundle fairness metrics and [mitigation algorithms](https://telnyx.com/learn-ai/bias-mitigation).

### Overfitting

Overfitting happens when a classifier learns the quirks of its training examples instead of the underlying pattern, so it scores well on training data and worse on new data. It is the high-variance end of a tradeoff that uses "bias" in a statistical sense, unrelated to fairness. Bias is the error from a model too simple for the pattern, and variance is how much the model changes when the training sample changes. Flexible models, those with many adjustable parts such as deep trees, lower bias and raise variance, as _An Introduction to Statistical Learning_ explains, and the [bias-variance tradeoff](https://telnyx.com/learn-ai/bias-variance-tradeoff) is the balance between the two. The guards are a test set the model never trained on and settings that limit flexibility, such as a maximum tree depth or a regularization penalty.

### Label quality

Label quality caps what any classifier can learn, since the labels are the definition of a right answer. Mislabeled training examples teach the wrong boundary, and mislabeled test examples make the evaluation wrong as well. Clear labeling guidelines, a second annotator on hard cases, and a review of the cases where annotators disagree are the usual defenses.

### Drift

Drift erodes a classifier after deployment. The inputs can shift, as when spammers adopt new wording, or the right answer for the same input can change, which is [concept drift](https://telnyx.com/learn-ai/concept-drift-vs-data-drift), and accuracy falls with no error message. Tracking the mix of predicted classes over time, and re-scoring a freshly labeled sample on a schedule, catches drift before it shows up as a business problem.

## Frequently asked questions

### What are 5 examples of classification?

Five common examples of AI classification are spam filtering, fraud screening, skin lesion diagnosis, sentiment analysis, and answering machine detection. Each picks from a short list of labels: spam or not, fraudulent or legitimate, malignant or benign, positive or negative or neutral, person or machine. Four of the five are binary, and sentiment analysis is multiclass. Tagging a support call with every topic it covers would be a multilabel example.

### What are the four main types of classification?

The four types most often listed are binary, multiclass, multilabel, and imbalanced classification. Binary picks one of two classes, multiclass picks one of three or more, and multilabel assigns any number of labels to the same input. Imbalanced classification is the odd one out. It describes data in which one class is rare, such as fraud, while the output stays binary or multiclass. What changes is how the model is trained and evaluated.

### Why is classification useful in AI?

Classification is useful because so many decisions reduce to choosing a category: approve or decline, spam or not, human or machine, which team should take a request. A classifier makes that choice consistently, at a volume no person could handle, and with a measurable error rate in each direction that can be tuned to the cost of each mistake. That measurability is what lets a team decide whether a classifier is safe to put in charge of a decision.

### Why is logistic regression called regression if it's used for classification?

Logistic regression is called regression because it models a number, the log-odds of the positive class, as a linear function of the features, and outputs a probability. Since it estimates class probabilities, "it can be thought of as a regression method," as _An Introduction to Statistical Learning_ notes. It becomes a classifier only when a threshold, 0.5 by default in scikit-learn, turns the probability into a label.

### Why don't we use linear regression for classification?

Linear regression makes a poor classifier for two reasons that _An Introduction to Statistical Learning_ sets out in section 4.2\. With more than two classes, coding them as 1, 2, and 3 imposes an order and equal spacing the categories do not have. Even with two classes, a straight-line fit produces values below 0 and above 1, which cannot be read as probabilities. Logistic regression fixes the second problem by passing the score through the sigmoid.

### Which algorithm is used for classification?

No single algorithm is used for classification; the choice depends on the data. Logistic regression is the usual baseline. Gradient boosting and random forests are strong on tabular data, neural networks on images and audio, and large language models on text when labeled examples are scarce. Support vector machines, k-nearest neighbors, and naive Bayes remain common where datasets are small or training speed matters.

### What is an AI classifier?

An AI classifier is a trained model that assigns inputs to classes, such as a spam filter or an image recognizer. The phrase also names a narrower product category: tools that try to classify text or audio as human-made or AI-generated. Those detectors have documented limits. [Liang and colleagues](https://arxiv.org/abs/2304.02819) found that seven widely used GPT detectors misclassified essays by non-native English writers as AI-generated at an average false positive rate of 61.22%.

### Can ChatGPT classify text?

ChatGPT and other large language models can classify text. Given the list of classes in a prompt, they return a label with no task-specific training, and a few labeled examples in the prompt often improve the result. Production systems hold the output to the label set, for example with a JSON schema, and measure precision and recall on labeled data, as they would for any other classifier.

### Is AI classification the same as classifying AI?

AI classification and the classification of AI are different things. AI classification is the machine learning task of assigning inputs to classes. Classifying AI means sorting AI systems themselves, by function, capability, or risk. By function, the four types are reactive machines, [limited memory AI](https://telnyx.com/learn-ai/limited-memory-ai), theory of mind AI, and self-aware AI. By risk, the [European Commission](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai) summarizes the EU AI Act as four levels, from unacceptable to minimal.

### What is AI data classification?

AI data classification labels information by how sensitive it is, so that access and protection rules can follow the label. The categories come from a policy, such as the low, moderate, and high impact levels in NIST's FIPS 199\. A classifier trained on labeled documents then assigns them at scale across files, email, and databases. It is the machine learning task applied to a security problem.

### Does Telnyx use AI classification?

Telnyx runs classification on live calls. Answering machine detection on the Telnyx Voice API labels each answered call as human or machine, and its Premium mode is built on machine learning. Deepfake Detection labels a caller's voice as real or AI-generated, with a probability score. For text, the Telnyx inference API can serve as an LLM classifier, with a JSON schema limiting answers to a fixed label set.

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[What is AI classification?](#what-is-ai-classification)[How does AI classification work?](#how-does-ai-classification-work)[Binary, multiclass, and multilabel classification](#binary-multiclass-and-multilabel-classification)[Classification vs regression vs clustering](#classification-vs-regression-vs-clustering)[Types of AI classification algorithms](#types-of-ai-classification-algorithms)[How to evaluate a classifier](#how-to-evaluate-a-classifier)[Real-world applications of AI classification](#real-world-applications-of-ai-classification)[Challenges in AI classification](#challenges-in-ai-classification)[Frequently asked questions](#frequently-asked-questions)[Sources](#sources)

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