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My Notes on MAE vs MSE Error Metrics πŸš€

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Manage episode 366510088 series 3474670
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/my-notes-on-mae-vs-mse-error-metrics.
We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #metrics, #linear-regression, #error-metrics, #machine-learning, #regularization, #normal-distribution, #residuals, #hackernoon-es, and more.
This story was written by: @sengul. Learn more about this writer by checking @sengul's about page, and for more stories, please visit hackernoon.com.
We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models. MAE is the average distance between the real data and the predicted data, but fails to punish large errors in prediction. MSE measures the average squared difference between the estimated values and the actual value. L1 and L2 Regularization is a technique used to reduce the complexity of the model. It does this by penalizing the loss function by regularizing the function of the function.

  continue reading

126 episodes

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Manage episode 366510088 series 3474670
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/my-notes-on-mae-vs-mse-error-metrics.
We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #metrics, #linear-regression, #error-metrics, #machine-learning, #regularization, #normal-distribution, #residuals, #hackernoon-es, and more.
This story was written by: @sengul. Learn more about this writer by checking @sengul's about page, and for more stories, please visit hackernoon.com.
We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models. MAE is the average distance between the real data and the predicted data, but fails to punish large errors in prediction. MSE measures the average squared difference between the estimated values and the actual value. L1 and L2 Regularization is a technique used to reduce the complexity of the model. It does this by penalizing the loss function by regularizing the function of the function.

  continue reading

126 episodes

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