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Metric is required to Evaluate a Machine Learning Model
There are many metrics to check how well a Regression Model behaves , But the most common one is RMSE
Here’s how you can check the accuracy of your next ML model
Thread 🧵
Taught in #MLZoomcamp by @Al_Grigor
There are many metrics to check how well a Regression Model behaves , But the most common one is RMSE
Here’s how you can check the accuracy of your next ML model
Thread 🧵
Taught in #MLZoomcamp by @Al_Grigor
RMSE - Root Mean Squared Error
It tells us the average loss/distance between our predicted values and target in the dataset
It's simply how much the trained model disagrees with the actual Target value
and It tells us how large the errors that our model makes are.
It tells us the average loss/distance between our predicted values and target in the dataset
It's simply how much the trained model disagrees with the actual Target value
and It tells us how large the errors that our model makes are.
RMSE is computed with the formula below in pic:
Here,
✔ ' Predicted' is the Value predicted by the Model
✔ 'Actual" is the Target value(y)
✔ 'N' is the number of observations in the dataset
Here,
✔ ' Predicted' is the Value predicted by the Model
✔ 'Actual" is the Target value(y)
✔ 'N' is the number of observations in the dataset
