What is Supervised Versus Unsupervised Learning in AI
This is the difference between what is supervised versus unsupervised learning in AI. Supervised and unsupervised learning are two fundamental approaches to machine learning in AI. In supervised learning, the machine learning model is trained on labeled data, which means that the input data is associated with a corresponding output label. On the other hand, unsupervised learning involves training the model on unlabeled data, where there is no predetermined output label.
Let's dive deeper into each of these approaches:
Supervised learning is the most commonly used approach in machine learning, where the model is trained on a labeled dataset. This means that the input data (features) is accompanied by a corresponding output label (target variable). The goal of supervised learning is to learn a mapping between the input data and the output label, so that given new input data, the model can accurately predict the corresponding output label.
Supervised learning algorithms can be further classified into two categories, based on the type of output variable:
- Classification: In classification, the output variable is categorical in nature. For example, predicting whether an email is spam or not, or classifying images of animals into different categories such as cats, dogs, or birds.
- Regression: In regression, the output variable is continuous in nature. For example, predicting the price of a house based on its features such as the number of bedrooms, bathrooms, etc.
Some of the commonly used supervised learning algorithms are: linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks.
Unsupervised learning is an approach to machine learning where the model is trained on an unlabeled dataset, where there is no predetermined output label. The goal of unsupervised learning is to discover patterns and relationships in the data, without any prior knowledge of the underlying structure.
Unsupervised learning algorithms can be further classified into two categories:
- Clustering: In clustering, the goal is to group similar data points together. For example, grouping customers based on their purchasing behavior, or clustering images based on their content.
- Association: In association, the goal is to discover relationships between variables in the data. For example, identifying which products are commonly bought together in a grocery store.
Some of the commonly used unsupervised learning algorithms are: k-means clustering, hierarchical clustering, principal component analysis (PCA), and association rule mining.
In summary, supervised learning is used when we have labeled data and want to predict a specific output, while unsupervised learning is used when we have unlabeled data and want to discover patterns and relationships in the data.
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