What different regression types are there?

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  • Adrianna
    Adrianna

    There are several different types of regression in statistics and machine learning, including:

    1. Linear regression is the simplest form of regression, where the goal is to find a linear relationship between a dependent variable and one or more independent variables.
    2. Multiple regression is similar to linear regression but involves more than one independent variable.
    3. Polynomial regression is a type of regression that involves fitting a polynomial function to the data rather than a straight line.
    4. Logistic regression is used when the dependent variable is categorical, such as binary (yes/no) or ordinal (low/medium/high).
    5. Ridge regression is a type of linear regression that uses a regularization technique to prevent overfitting of the data.
    6. Lasso regression is similar to ridge regression but uses a different type of regularization that can lead to sparse models (i.e., models with many coefficients set to zero).
    7. Elastic net regression combines ridge and lasso regression that can help balance their strengths and weaknesses.
    8. Time series regression is used when the data is time-dependent, and the goal is to predict future values of the dependent variable based on past values.
    9. Bayesian regression uses Bayesian statistical methods to estimate the parameters of the regression model and can provide uncertainty estimates for the predictions.
    10. Nonparametric regression is flexible and does not assume a specific functional form for the relationship between the dependent and independent variables.

    Here's how you can build a regression model in Symon.AI: https://www.symon.ai/learning-hub/build-a-regression-model

    The docs are the most up-to-date: https://app.symon.ai/assets/docs/en/tools/predict/regressor.html

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