Which model is suitable for predicting a continuous outcome?

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BlackTom AI · Accepted Answer

To identify a model suitable for predicting a continuous outcome, we evaluate what each option is designed to do.
Option 1: Linear Regression. This algorithm is specifically used for regression tasks where the target variable is continuous. It fits a linear relationship between the input features and the continuous output, making it appropriate for predicting quantities like height, price, or temperature.
Option 2: Logistic Regression. Despite its name, this method is used for classification problems, typically when the outcome is binary or categorical. It estimates the probability of class membership rather than predicting a numeric, continuous value.
Option 3: Naive Bayes. This is a probabilistic classifier based on Bayes' theorem with strong independence assumptions among features. It is best suited for classification tasks (e.g., spam detection, sentiment analysis) rather than predicting a numerical outcome.
Option 4: Decision Tree (classification). This refers to a tree-structured model used for classification. While decision trees can be adapted for regression (known as regression trees or CART for regression), the phrasing here specifies classification, which targets discrete labels rather than a continuous numeric prediction.
In summary, among the given choices, Linear Regression is the model designed for predicting continuous outcomes, whereas the others are geared toward classification or probabilistic inference rather than direct numeric prediction.

Which model is suitable for predicting a continuous outcome?

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