binary classification
In plain wordsSorting 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.
The classificationclassification, n.: Sorting 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. task with exactly two classes, conventionally called positive and negative (coded 1/0 or +1/−1). Most binary classifiers compute a real-valued score — a probability from 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., a signed distance from a support vector machinesupport 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. — and predict positive when it exceeds a threshold. Moving the threshold trades false positivesfalse positive, n.: A false alarm: a test or AI system raises a flag, for example for illness, fraud or danger, when nothing of the kind is really there. against false negatives, so binary classifiers are assessed both across all thresholds, with 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. and precision–recallprecision–recall curve, n.: A graph showing how a model's share of correct alarms and its share of cases caught trade off as it is made more or less cautious; especially useful when positive cases are rare. curves and the areas under them, and at a chosen operating point, with 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. and the confusion matrixconfusion matrix, n.: A table that counts a model's right and wrong answers by type, showing for each true category how often each category was predicted, so you can see exactly which things it mixes up.. Many multiclass and multilabel methods are assembled from binary classifiers, as in one-vs-restone-vs-rest, n.: A way to handle many categories with a method that only gives yes-or-no answers: train one detector per category ('this one or not?') and pick the category whose detector is most confident..
We treat churn as binary classification and set the threshold where recall reaches 90%.
Origin
The two-group problem is the oldest case of statistical classification: Ronald Fisher's 1936 linear discriminant was formulated for two groups. Signal detection theory, developed in 1950s radar and psychophysics research, supplied the vocabulary of hits, false alarms and ROC curves later used to evaluate binary classifiers.
Usage note
Choosing the threshold is a business or policy decision, not a property of the model; the default of 0.5 is rarely optimal when classes are imbalanced or errors have unequal costs.
Related
- classificationclassification, n.: Sorting 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.
- 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.
- 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.
- ROC curveROC 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.
- precision–recall curveprecision–recall curve, n.: A graph showing how a model's share of correct alarms and its share of cases caught trade off as it is made more or less cautious; especially useful when positive cases are rare.
- confusion matrixconfusion matrix, n.: A table that counts a model's right and wrong answers by type, showing for each true category how often each category was predicted, so you can see exactly which things it mixes up.
- 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.
- 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.
Mentioned in
classificationclassification, n.: Sorting 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., AdaBoostAdaBoost, prop. n.: One of the first practical boosting methods: it trains simple models in turn, giving extra weight to examples the earlier models got wrong, then lets all the models vote, weighted by accuracy., 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., confusion matrixconfusion matrix, n.: A table that counts a model's right and wrong answers by type, showing for each true category how often each category was predicted, so you can see exactly which things it mixes up., 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., 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., one-vs-restone-vs-rest, n.: A way to handle many categories with a method that only gives yes-or-no answers: train one detector per category ('this one or not?') and pick the category whose detector is most confident., perceptronperceptron, n.: One of the first learning machines: a single artificial neuron that sorts inputs into two groups by drawing a straight dividing line, nudging its weights whenever it guesses wrong., sigmoid functionsigmoid function, n.: An S-shaped curve that squeezes any number into a value between 0 and 1, so the result can be read as a probability or as how far a switch is turned on., support vector machinesupport 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.