Should test data be scaled?
Yes, scaling should be done on both the training data and the test data. Additionally, the scaling should be the same. If you scale the training set one way and the testing set another way, this will still create issues.
When should we scale our data?
You want to scale data when you’re using methods based on measures of how far apart data points, like support vector machines, or SVM or k-nearest neighbors, or KNN. With these algorithms, a change of “1” in any numeric feature is given the same importance.
Should data be scaled for logistic regression?
The performance of logistic regression did not improve with data scaling. The reason is that, if there predictor variables with large ranges that do not effect the target variable, a regression algorithm will make the corresponding coefficients ai small so that they do not effect predictions so much.
Do you scale both training and test data?
The test set must use identical scaling to the training set. And the point is given that: Do not scale the training and test sets using different scalars: this could lead to random skew in the data. Could someone explain what that means?
How do we scale our dataset correctly in pseudo code?
Now, a commonly asked question is how we scale our dataset correctly. For simplicity, I will write the examples in pseudo code using the “standardization” procedure. However, note that the same principles apply to other scaling methods such as min-max scaling.
What does it mean to standardize a dataset?
Standardizing a dataset involves rescaling the distribution of values so that the mean of observed values is 0 and the standard deviation is 1. It is sometimes referred to as “ whitening .” This can be thought of as subtracting the mean value or centering the data.
What happens when data comes from different distribution?
This time, it is because the classifier performs well on a dataset it hasn’t seen before if it comes from the same distribution, such as the bridge set. It performs poorly if it comes from a different distribution, like the dev set. Thus, we have a data mismatch problem.