Having three evaluation metrics makes it harder for you to quickly choose between two different algorithms, and will slow down the speed with which your team can iterate. True/False?

Typically, the $$R^2$$ score is sufficient for evaluating a regression model. Alternative evaluation metrics include:
- Mean Absolute Error (MAE): The absolute difference between target and predicted values, representing the expected value of $$L_1$$ norm loss.
- Mean Squared Error (MSE): The squared difference between target and predicted values, representing the expected value of $$L_2$$ norm loss.
- Median Absolute Error: Uses the median of the error distribution rather than the mean, making it more robust to outliers.

Characteristics of Regression Evaluation Metrics

- Mean Squared Error(MSE)
- Root-Mean-Squared-Error(RMSE).
- Mean-Absolute-Error(MAE).
- R² or Coefficient of Determination.
- Adjusted R²

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Regression models predict a value Y given a set of X variables.
In the regression model $Y \sim X$, we measure the effect of $X$ on $Y$, i.e., we measure the association between changes in values of $X$ and changes in values of $Y$.
Analyzing a regression model is a method used to solve a regression problem.

Regression Models

Regression Model Evaluation Metrics

In the regression model $Y \sim X$, to control for the effect of confounder $Z$ (where $X \leftarrow Z \rightarrow Y$) we can use the regression model $Y \sim X + Z$, i.e., taking the average effect of $X$ on $Y$ across differnt values of $Z$.

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