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Regression

Apr 30, 2024, 2 min read

Regression

Group: 4 #group-4

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  • Polynomial Regression: Polynomial regression is a type of regression analysis that models the non-linear relationship between a dependent variable and one or more independent variables using polynomial functions.
  • Non-linear Regression: Non-linear regression is a general term for regression models that model non-linear relationships between variables using various functions, such as exponential, logarithmic, or power functions.
  • Machine Learning: Regression is a type of Supervised Learning task where the goal is to predict a continuous numerical value based on input features.
  • Regression Diagnostics: Regression diagnostics are a set of techniques used to assess the validity of the assumptions and the overall fit of a regression model, including residual analysis, influence diagnostics, and model selection criteria.
  • Time Series Analysis: Regression techniques are used in time series analysis to model and forecast time-dependent data.
  • Predictive Analytics: Regression models are used in predictive analytics to make predictions based on historical data.
  • Decision Trees: Decision Trees can also be used for Regression tasks
  • Statistical Modeling: Regression is a statistical modeling technique used to analyze the relationship between variables.
  • Machine Learning: Regression is a type of Supervised Learning task used to predict a continuous numerical value.
  • Regularization: Regularization techniques, such as ridge regression and lasso regression, are used to prevent overfitting in regression models by adding a penalty term to the cost function.
  • Linear Regression: Linear regression is a type of regression analysis that models the linear relationship between a dependent variable and one or more independent variables.
  • Machine Learning: Regression is a fundamental technique in machine learning for supervised learning tasks.
  • Logistic Regression: Logistic regression is a type of regression analysis used for binary classification problems, where the dependent variable is categorical.

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