Regression Algorithm 편집하기 (부분)

==Types of Regression Algorithms==
There are several types of regression algorithms, each suited to different types of data and relationships:
*'''[[Linear Regression]]''': The most basic regression technique, which assumes a linear relationship between the input variables (features) and the output variable. It tries to fit a straight line through the data points that best predicts the output.
*'''Polynomial Regression''': Extends linear regression by fitting a polynomial curve to the data, making it suitable for datasets where the relationship between variables is non-linear.
*'''Ridge Regression''': A regularized version of linear regression that includes a penalty term to prevent overfitting, especially useful for high-dimensional data.
*'''Lasso Regression''': Similar to Ridge Regression but uses a different penalty that can shrink coefficients to zero, allowing it to perform feature selection.
*'''[[Logistic Regression]]''': Often used for binary classification rather than regression. Despite its name, it predicts the probability of a binary outcome using a logistic function.
*'''Support Vector Regression (SVR)''': A regression version of Support Vector Machines, which tries to fit the best line within a specific margin to predict continuous values.
*'''Decision Tree Regression''': Uses a tree structure where data is split at each node based on feature values, suitable for capturing complex, non-linear relationships.
*'''Random Forest Regression''': An ensemble method using multiple decision trees to improve accuracy and reduce overfitting.
*'''Neural Network Regression''': A deep learning approach, suitable for complex data patterns where other methods may not perform well.
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