https://github.com/sshbuilder/learningregression
This is the repo where i am going try out new and experimental regression techniques and document my learning as much as possible
https://github.com/sshbuilder/learningregression
Last synced: 3 months ago
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This is the repo where i am going try out new and experimental regression techniques and document my learning as much as possible
- Host: GitHub
- URL: https://github.com/sshbuilder/learningregression
- Owner: sshBuilder
- Created: 2024-02-09T03:52:20.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-22T05:22:19.000Z (about 1 year ago)
- Last Synced: 2025-02-26T19:49:12.143Z (3 months ago)
- Language: Jupyter Notebook
- Size: 34.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Learning Linear Regression
1. **Linear Regression**:
Linear regression is perhaps the most fundamental and widely used regression model. It assumes a linear relationship between the independent variables and the dependent variable, represented by a straight line. The model estimates the coefficients of the linear equation that best fits the observed data, allowing for prediction and inference.
2. **Multiple Regression**:
Multiple regression extends the concept of linear regression to cases where there are multiple independent variables. It enables the analysis of how multiple predictors simultaneously affect the dependent variable, providing insights into complex relationships and interactions.
3. **Polynomial Regression**:
Polynomial regression allows for the fitting of nonlinear relationships between variables by using polynomial functions. It is useful when the relationship between the independent and dependent variables cannot be adequately captured by a straight line, offering greater flexibility in modeling complex data patterns.
4. **Logistic Regression**:
Logistic regression is used when the dependent variable is binary or categorical. Unlike linear regression, which predicts continuous outcomes, logistic regression models the probability of a binary outcome based on one or more independent variables. It is widely used in classification tasks and binary outcome prediction.
5. **Ridge Regression**:
Ridge regression is a regularization technique used to address multicollinearity and overfitting in linear regression models. It adds a penalty term to the regression equation, which shrinks the coefficients towards zero, particularly for variables with high multicollinearity.
6. **Lasso Regression**:
Lasso regression, similar to ridge regression, is a regularization technique used to prevent overfitting and select important features in the data. It adds a penalty term that encourages sparsity in the coefficient estimates by forcing some coefficients to be exactly zero.
7. **Elastic Net Regression**:
Elastic net regression combines the penalties of ridge and lasso regression, offering a compromise between the two techniques. It is useful when there are multiple correlated independent variables and provides a balance between model simplicity and predictive accuracy.
8. **Nonlinear Regression**:
Nonlinear regression is used when the relationship between the independent and dependent variables is nonlinear. It involves fitting a nonlinear function to the data, allowing for more flexible modeling of complex data patterns and relationships.
Each regression model has its own assumptions, advantages, and limitations, and the choice of model depends on the nature of the data and the objectives of the analysis. By understanding the principles and applications of different regression models, analysts can make informed decisions and derive meaningful insights from their data. In the ever-evolving landscape of data analytics, regression models remain indispensable tools for understanding the complexities of the world around us and unlocking actionable insights from data.