Terminologies Related to the Regression Analysis in Machine Learning
Terminologies Related to Regression Analysis:
- Response Variable: The primary factor to predict or understand in regression, also known as the dependent variable or target variable.
- Predictor Variable: Factors influencing the response variable, used to predict its values; also called independent variables.
- Outliers: Observations with significantly low or high values compared to others, potentially impacting results and best avoided.
- Multicollinearity: High correlation among independent variables, which can complicate the ranking of influential variables.
- Underfitting and Overfitting: Overfitting occurs when an algorithm performs well on training but poorly on testing, while underfitting indicates poor performance on both datasets.
Regression Types
There are two main types of regression:
- Simple Regression
- Used to predict a continuous dependent variable based on a single independent variable.
- Simple linear regression should be used when there is only a single independent variable.
- Multiple Regression
- Used to predict a continuous dependent variable based on multiple independent variables.
- Multiple linear regression should be used when there are multiple independent variables.
- NonLinear Regression
- Relationship between the dependent variable and independent variable(s) follows a nonlinear pattern.
- Provides flexibility in modeling a wide range of functional forms.
Regression in machine learning
Regression, a statistical approach, dissects the relationship between dependent and independent variables, enabling predictions through various regression models.
The article delves into regression in machine learning, elucidating models, terminologies, types, and practical applications.
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