Most ML Folks Often Neglect This While Using Linear Regression
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The effectiveness of a linear regression model is determined by how well the data conforms to the algorithm's underlying assumptions. One highly important, yet often neglected assumption of linear regression is homoscedasticity. A dataset is homoscedastic if the variability of residuals (=actual-predicted) stays the same across the input range.
Most ML Folks Often Neglect This While Using Linear Regression
Most ML Folks Often Neglect This While Using…
Most ML Folks Often Neglect This While Using Linear Regression
The effectiveness of a linear regression model is determined by how well the data conforms to the algorithm's underlying assumptions. One highly important, yet often neglected assumption of linear regression is homoscedasticity. A dataset is homoscedastic if the variability of residuals (=actual-predicted) stays the same across the input range.