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Questions and Answers
What is the goal of supervised machine learning regression?
What is the goal of supervised machine learning regression?
- To optimize the number of observations
- To find the regression coefficients that produce the smallest SSE (correct)
- To calculate the linear correlation coefficient
- To minimize the variation in the independent variables
What is the difference between SSE and SSR in supervised machine learning?
What is the difference between SSE and SSR in supervised machine learning?
- SSE is the total sum of squares, while SSR is the mean squared error
- SSE is the loss function, while SSR is the correlation coefficient
- SSE is the explained variation, while SSR is the unexplained variation
- SSE is the sum of the squared errors, while SSR is the sum of the squared residuals (correct)
What is the role of loss function in supervised machine learning?
What is the role of loss function in supervised machine learning?
- To determine the accuracy of the supervised machine learning model (correct)
- To minimize the variation in the independent variables
- To optimize the number of observations
- To calculate the linear correlation coefficient
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Study Notes
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.
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