classification
In plain wordsSorting things into categories — spam or not spam, which animal is in a photo, which language a text is written in — by learning from examples that are already labeled.
- 1
Machine learning
A supervised learningsupervised learning, n.: Teaching a computer by example: you give it many inputs paired with the right answers, such as photos labeled 'cat' or 'dog', and it learns to answer correctly for new inputs. task in which a model assigns each input to one of a finite set of discrete categories (classes). A classification model, or classifierclassifier, n.: A program or model that sorts inputs into categories, for example flagging an email as spam or a chatbot message as harmful. Many AI safety filters are classifiers., is trained on labeled examples and outputs either a hard label or a score or probability for each class, which a decision rule — usually a threshold or an arg max — turns into a label. Problems are distinguished by label structure: binary classificationbinary classification, n.: Sorting things into exactly two groups, such as spam versus not spam or sick versus healthy. Most yes-or-no decisions made by AI systems are of this kind. (two classes), multiclass classificationmulticlass classification, n.: Sorting each item into exactly one of three or more categories — for example, deciding which of the ten digits a handwritten number shows. (exactly one of many) and multilabel classificationmultilabel classification, n.: Tagging each item with any number of labels at once — a photo can show a beach, a dog and a sunset — instead of picking just one category. (any subset of labels). Common methods include logistic regressionlogistic regression, n.: A simple, widely used method for yes-or-no predictions that outputs a probability — say, a 70% chance a customer cancels — by passing a weighted sum of inputs through an S-shaped curve., decision treesdecision tree, n.: A model that makes a prediction by asking a series of yes-or-no questions about the input, like a flowchart, with each answer leading to the next question until it reaches a final answer. and their ensembles, support vector machinessupport vector machine, n.: A classic method that separates two groups of examples with the widest possible gap — like laying the broadest possible road between them. A mathematical shortcut lets it draw curved boundaries too., k-nearest neighborsk-nearest neighbors, n.: Predicting something about a new example by finding the few most similar examples already known and going with their majority answer, or their average., naive Bayesnaive Bayes, n.: A simple, fast way of sorting items into categories using probabilities, which pretends that each clue is independent of the others. It is the classic technique behind early email spam filters. and neural networks ending in a softmaxsoftmax, n.: A formula that turns a list of raw scores into probabilities — all positive and adding up to one — with the biggest share going to the highest score. layer. Results are evaluated with accuracyaccuracy, n.: The share of a model's answers that are right, such as 95 out of 100. Simple and intuitive, but misleading when one kind of answer is far more common than the others., precisionprecision, n.: Of all the items a model flagged as positive, the share that really were positive; a spam filter with high precision rarely sends genuine emails to the spam folder., recallrecall, n.: Of all the items that really were positive, the share the model managed to find; a cancer screen with high recall misses very few people who actually have cancer., F1 scoreF1 score, n.: A single score that combines how many of a model's alarms are right and how many real cases it catches, and stays low unless both are good., ROCROC curve, n.: A graph showing how a detector's hit rate and false-alarm rate change together as it is made more or less trigger-happy; the closer the curve hugs the top-left corner, the better. analysis and calibrationcalibration, n.: How well a model's stated confidence matches reality: when a well-calibrated system says it is 80% sure, it turns out to be right about 80% of the time..
We framed fraud detection as classification, but with 0.1% positives, accuracy alone is meaningless.
- 2
Older and general usage
The construction of a system of categories itself, as in biological taxonomy or 'numerical classification'. In this sense, still found in older statistics and some sciences, it overlaps with what machine learning calls clusteringclustering, n.: Automatically sorting things into groups of similar items when nobody has said in advance what the groups should be, like sorting a drawer of odd socks into piles by color and size.; the machine-learning sense presupposes that the categories are already given.
Origin
Statistical classification descends from Ronald Fisher's 1936 discriminant analysis of iris measurements and from 1950s–1960s pattern recognition, where perceptrons and nearest-neighbor rules were studied as trainable classifiers. Richard Duda and Peter Hart's textbook 'Pattern Classification and Scene Analysis' (1973) helped fix the vocabulary of supervised classification.
Usage note
Many AI tasks in the news — content moderation, sentiment analysissentiment analysis, n.: Using software to judge the feelings or opinions expressed in text — whether a review, post or comment is positive, negative or neutral, and about what., image classificationimage classification, n.: Teaching a computer to say what a picture shows by choosing a label, such as 'cat', 'pneumonia' or 'stop sign', from a fixed list of possibilities., safety classifierssafety classifier, n.: A separate AI model that checks messages going into or coming out of a chatbot and flags or blocks those that break safety rules, like a security guard at the door. — are classification. Not to be confused with clusteringclustering, n.: Automatically sorting things into groups of similar items when nobody has said in advance what the groups should be, like sorting a drawer of odd socks into piles by color and size., where no labels are given and the groups are discovered.
Related
- classifierclassifier, n.: A program or model that sorts inputs into categories, for example flagging an email as spam or a chatbot message as harmful. Many AI safety filters are classifiers.
- binary classificationbinary classification, n.: Sorting things into exactly two groups, such as spam versus not spam or sick versus healthy. Most yes-or-no decisions made by AI systems are of this kind.
- multiclass classificationmulticlass classification, n.: Sorting each item into exactly one of three or more categories — for example, deciding which of the ten digits a handwritten number shows.
- multilabel classificationmultilabel classification, n.: Tagging each item with any number of labels at once — a photo can show a beach, a dog and a sunset — instead of picking just one category.
- decision boundarydecision boundary, n.: The dividing line, or surface, where a classifier switches from predicting one category to another. Simple models draw straight lines; flexible ones draw curves.
- supervised learningsupervised learning, n.: Teaching a computer by example: you give it many inputs paired with the right answers, such as photos labeled 'cat' or 'dog', and it learns to answer correctly for new inputs.
- regressionregression, n.: Predicting a number rather than a category — a house's price, tomorrow's temperature, a patient's blood pressure — by learning from past examples where the answer is known.
- regressionregression, n.: Predicting a number rather than a category — a house's price, tomorrow's temperature, a patient's blood pressure — by learning from past examples where the answer is known.
- clusteringclustering, n.: Automatically sorting things into groups of similar items when nobody has said in advance what the groups should be, like sorting a drawer of odd socks into piles by color and size.
Sources
- Fisher, R. A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.
- Duda, R. O. & Hart, P. E. (1973). Pattern Classification and Scene Analysis. Wiley.
Mentioned in
accuracyaccuracy, n.: The share of a model's answers that are right, such as 95 out of 100. Simple and intuitive, but misleading when one kind of answer is far more common than the others., classifierclassifier, n.: A program or model that sorts inputs into categories, for example flagging an email as spam or a chatbot message as harmful. Many AI safety filters are classifiers., regressionregression, n.: Predicting a number rather than a category — a house's price, tomorrow's temperature, a patient's blood pressure — by learning from past examples where the answer is known., baggingbagging, n.: Training many copies of a model, each on a different random resample of the data, and averaging their predictions. The averaging cancels out much of each copy's random error., binary classificationbinary classification, n.: Sorting things into exactly two groups, such as spam versus not spam or sick versus healthy. Most yes-or-no decisions made by AI systems are of this kind., class imbalanceclass imbalance, n.: When one category is far rarer than the others in the data — say, one fraud case per thousand transactions — so a model can look accurate while missing nearly every rare case that matters., clusteringclustering, n.: Automatically sorting things into groups of similar items when nobody has said in advance what the groups should be, like sorting a drawer of odd socks into piles by color and size., data miningdata mining, n.: Searching large collections of data with computers to discover useful patterns, such as products often bought together or signs that a transaction may be fraudulent., k-nearest neighborsk-nearest neighbors, n.: Predicting something about a new example by finding the few most similar examples already known and going with their majority answer, or their average., linear discriminant analysislinear discriminant analysis, n.: A statistical method for telling known groups apart. It finds the weighted mix of measurements on which groups differ most relative to their spread, then uses it to sort new items or compress many measurements into a few., multiclass classificationmulticlass classification, n.: Sorting each item into exactly one of three or more categories — for example, deciding which of the ten digits a handwritten number shows., multilabel classificationmultilabel classification, n.: Tagging each item with any number of labels at once — a photo can show a beach, a dog and a sunset — instead of picking just one category., naive Bayesnaive Bayes, n.: A simple, fast way of sorting items into categories using probabilities, which pretends that each clue is independent of the others. It is the classic technique behind early email spam filters., pattern recognitionpattern recognition, n.: The ability of a computer, or a brain, to spot regularities in data and sort things into categories, such as recognizing handwritten letters, faces or spoken words., predictionprediction, n.: A model's output for a given input: its best guess at an unknown answer. In AI it need not be about the future; labeling a photo 'cat' counts as a prediction., sentiment analysissentiment analysis, n.: Using software to judge the feelings or opinions expressed in text — whether a review, post or comment is positive, negative or neutral, and about what., supervised learningsupervised learning, n.: Teaching a computer by example: you give it many inputs paired with the right answers, such as photos labeled 'cat' or 'dog', and it learns to answer correctly for new inputs., text classificationtext classification, n.: Sorting pieces of text into categories automatically — what topic an article covers, whether a message is spam, which team should handle a customer's complaint., Bayes error rateBayes error rate, n.: The lowest error any classifier could ever achieve on a task, because some cases are genuinely ambiguous: even a perfect model that knew the true odds would still sometimes guess wrong., CARTCART, abbr.: A classic recipe for building decision trees that splits data two ways at every step, works for both categories and numbers, and then trims the tree back so it does not memorize noise., hinge losshinge loss, n.: A way of scoring a classifier's mistakes that charges nothing for answers that are right with room to spare, and charges more and more the further an answer falls on the wrong side.