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Measures of Error in Supervised Machine Learning

• Linear correlation coefficient is the standardized slope of the simple linear regression line.
• To determine the accuracy of a supervised machine learning model, a loss function is applied to predicted values and observed values.
• Error for each prediction is calculated by subtracting the observed value from the predicted value.
• Three types of measures of error are used: Sum of Squared Errors (SSE), Sum of Squared Residuals (SSR), and Total Sum of Squares.
• SSE is calculated by squaring the error of each observation and adding them up.
• The goal is to find the regression coefficients that produce the smallest SSE.
• Mean Squared Error (MSE) is calculated by dividing SSE by the number of observations.
• Regression-based supervised machine learning aims to optimize regression coefficients to minimize the MSE.
• Linear regression can be done in Python using Linear Least Squares Method.
• Variation in supervised machine learning regression models can be explained by independent variables or not.
• Explained variation is also called SSR, while unexplained variation is called SSE.
• Supervised machine learning models can be used for prediction or interpretation, and modeling best practices include developing multiple models and comparing them based on a cost function.

Test your knowledge on the measures of error in supervised machine learning with this informative quiz. Explore the different types of measures of error, including Sum of Squared Errors, Sum of Squared Residuals, and Total Sum of Squares, and learn how they are used to determine the accuracy of a supervised machine learning model. Discover the importance of linear regression and how it can be optimized using the Linear Least Squares Method in Python. Test your understanding of regression-based supervised machine learning and the concept of