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Classification models are used to categorize data into classes from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Classification models are used to categorize data into classes based on their features. These models are essential in data science as they help in predicting the class of new data points and making decisions based on the predicted classes. For example, a classification model can be used to predict whether an email is spam or not spam based on its content and other features. There are different types of classification models, such as logistic regression, decision trees, support vector machines, and neural networks. Each model has its strengths and weaknesses, and the choice of model depends on the nature of the data and the problem at hand. Logistic regression is a simple and interpretable model that works well with linearly separable data. Decision trees are easy to understand and can handle nonlinear relationships between features. Support vector machines are powerful models that work well with high-dimensional data. Neural networks are complex models that can learn intricate patterns in the data but requ...
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