What are the performance metrics that can be used to estimate the efficiency of a linear regression model?

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The performance metric that is used in this case is:

  • Mean Squared Error
  • R2 score
  • Adjusted R2 score
  • Mean Absolute score

 

In the context of a machine learning interview, when asked about performance metrics for evaluating the efficiency of a linear regression model, you can mention several commonly used metrics. Here are some key ones:

  1. Mean Squared Error (MSE):
    • Formula: MSE=n1​∑i=1n​(yi​−y^​i​)2
    • Explanation: It calculates the average squared difference between the predicted and actual values. Lower MSE indicates better performance.
  2. Root Mean Squared Error (RMSE):
    • Formula:  RMSE=MSERMSE=MSE​​
    • Explanation: Similar to MSE but provides the error in the same units as the target variable. It is also interpretable in the context of the target variable.
  3. Mean Absolute Error (MAE):
    • Formula: MAE=n1​∑i=1n​∣yi​−y^​i​∣
    • Explanation: Measures the average absolute difference between the predicted and actual values. It is less sensitive to outliers than MSE.
  4. R-squared (R2) Score:
    • Formula: R2=1−∑i=1n​(yi​−yˉ​)2∑i=1n​(yi​−y^​i​)2​​
    • Explanation: Represents the proportion of the variance in the dependent variable that is predictable from the independent variable. R2 score ranges from 0 to 1, with higher values indicating better model fit.

During the interview, you can discuss these metrics and emphasize the importance of selecting metrics based on the specific characteristics of the problem at hand, considering factors such as the presence of outliers, the scale of the target variable, and the interpretability of the metric. Additionally, it’s crucial to mention that the choice of metrics depends on the goals and requirements of the specific application.