Machine Learning Regression Analysis Data Science Education Technology
Levi
Levi

@levikul09

8 tweets 63 reads Aug 17, 2023
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5 Regression Algorithms you should know
🧡
5 Regression Algorithms you should know 🧡 https://t.co/DACn4b6QJ5
1️⃣ Linear
Linear regression is the most fundamental and widely used regression algorithm.
It assumes a linear relationship between the variables.
The goal is to find the best-fitting line that minimizes the errors between the predicted and actual values.
1️⃣ Linear Linear regression is the most fundamental and widely used regression algorithm. It ass...
2️⃣ Polynomial
Polynomial regression allows for nonlinear relationships between variables.
It adds polynomial functions to the line equation, so it can capture more complex patterns in the data.
2️⃣ Polynomial Polynomial regression allows for nonlinear relationships between variables. It add...
3️⃣ Ridge
Ridge regression is useful when dealing with high-dimensional datasets.
Why?
It adds a penalty term to the linear regression cost function, which helps reduce the impact of irrelevant variables.
It is more robust to collinearity and overfitting.
3️⃣ Ridge Ridge regression is useful when dealing with high-dimensional datasets. Why? It adds a...
4️⃣ Lasso
Lasso regression is similar to Ridge, but it can exclude useless variables from equations.
It is particularly valuable when the number of predictors is large relative to the number of observations.
5️⃣ SVR - Support Vector
SVR aims to find a hyperplane that maximizes the margin around the predicted values.
SVR is based on the principles of support vector machines.
It is particularly effective when dealing with datasets with nonlinearity and outliers.
5️⃣ SVR - Support Vector SVR aims to find a hyperplane that maximizes the margin around the predic...
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