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Classification model: Difference between revisions
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{{see also|Machine learning terms}} | |||
==Introduction== | ==Introduction== | ||
In [[machine learning]], a [[classification model]] predicts which [[class]] a new [[input]] belongs to. In contrast, [[regression model]]s predict numbers rather than [[classes]]. | In [[machine learning]], a [[classification model]] predicts which [[class]] a new [[input]] belongs to. In contrast, [[regression model]]s predict numbers rather than [[classes]]. | ||
==What is a Classification Model?== | ==What is a Classification Model?== | ||
Classification models are machine learning algorithms that take input data and predict which class it belongs in. The input usually consists of | Classification models are machine learning algorithms that take input data and predict which class it belongs in. The input usually consists of [[feature]]s or attributes, while the [[output]] is a class [[label]]. The purpose of a classification model is to develop an algorithm that maps input data onto an accurate class label. | ||
Supervised learning utilizes both input features and their corresponding class labels as training data. The classification model is then trained on this labeled data, then applied to make predictions on new, unseen data. | [[Supervised learning]] utilizes both input features and their corresponding class labels as [[training data]]. The classification model is then trained on this labeled data, then applied to make predictions on new, unseen data. | ||
==Types of Classification Models== | ==Types of Classification Models== | ||
There are various classification models, such as: | There are various classification models, such as: | ||
#[[Binary classification]]: These models attempt to predict between two possible classes. | |||
#[[Multi-class classification]]: These models predict between more than two distinct classes. | |||
#[[Probabilistic classification]]: These models estimate the likelihood that an input belongs to each class. | |||
#[[Decision tree|Decision tree classification]]: These models employ a decision tree to classify input data based on an array of decisions. | |||
==How Classification Models Work== | ==How Classification Models Work== | ||
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Classification models have many practical uses, such as: | Classification models have many practical uses, such as: | ||
#Spam detection: Utilizing a classification model, email content can be classified as either spam or not spam. | |||
#Credit Risk Assessment: Utilizing a classification model, one can predict an applicant's creditworthiness based on their financial history and other factors. | |||
#Medical Diagnosis: Utilizing a classification model, medical images or other data can be classified as indicative of an underlying disease or condition. | |||
#Sentiment Analysis: A classification model can be utilized to categorize social media posts or reviews as positive, negative, or neutral. | |||
==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||
Classification models work like teachers who can look at pictures and identify what it is. After years of practice, they've come to recognize patterns in pictures that indicate whether it's of a cat, dog, or bird. When presented with new data sets, classifiers use what they've learned to predict which group it belongs in; for instance, looking at information about someone could potentially predict whether or not they will pay back loans. | Classification models work like teachers who can look at pictures and identify what it is. After years of practice, they've come to recognize patterns in pictures that indicate whether it's of a cat, dog, or bird. When presented with new data sets, classifiers use what they've learned to predict which group it belongs in; for instance, looking at information about someone could potentially predict whether or not they will pay back loans. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | |||