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What is supervised learning?
Supervised learning is a machine learning paradigm where an algorithm is trained using a dataset that contains input-output pairs. The primary goal is for the model to learn from these examples to make accurate predictions or classifications on new, unseen data. During the training process, the mode…
Headline
Supervised learning is a machine learning paradigm where an algorithm is trained using a dataset that contains input-output pairs. The primary goal is for the model to learn from these examples to make accurate predictions or classifications on new, unseen data. During the…
Context
Supervised learning is a machine learning paradigm where an algorithm is trained using a dataset that contains input-output pairs. The primary goal is for the model to learn from these examples to make accurate predictions or classifications on new, unseen data. During the training process, the model adjusts its parameters to minimise the error between its predictions and the actual outcomes provided in the training data. This iterative process continues until the model achieves a satisfactory level of accuracy. Various algorithms are used in supervised learning, each suitable for different types of tasks:
Evidence
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Analysis
Linear regression: Used for predicting a continuous value, such as estimating house prices based on features like square footage and location. Linear regression models the relationship between input variables and a continuous output. Logistic regression: Despite its name, logistic regression is used for binary classification tasks, such as determining whether an email is spam or not. It estimates the probability of a binary outcome based on input features. Decision trees: These models make decisions by splitting data into subsets based on feature values, forming a tree-like structure of decisions. They are versatile and can be used for both classification and regression tasks. Support vector machines: SVMs are used for classification tasks by finding the hyperplane that best separates different classes in the feature space. They are effective for high-dimensional data and complex classification problems.
Key Points
- Supervised learning is a type of machine learning where models are trained on labelled data to predict outcomes or classify new data based on past examples.
- It involves using algorithms to learn a mapping from inputs to outputs, making it one of the most common techniques in data science and AI.
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