What is quasi constant features?
Quasi-constant features, as the name suggests, are the features that are almost constant. In other words, these features have the same values for a very large subset of the outputs. Such features are not very useful for making predictions.
What’s the trade off between bias and variance?
You now know that: Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance.
Does mean deviation always sum up to zero?
The sum of the deviations from the mean is zero. This will always be the case as it is a property of the sample mean, i.e., the sum of the deviations below the mean will always equal the sum of the deviations above the mean. However, the goal is to capture the magnitude of these deviations in a summary measure.
Can a feature be removed because of low variance?
The features that are removed because of low variance have very low variance, that would be near to zero. You should always perform all the tests with existing data before discarding any features. Variables which are all 0’s or have near to zero variance can be dropped due to less predictive power. In some cases it might cause a problem as well.
Why do you remove low variance variables in machine learning?
If variables represent different physical quantities their scaling can be different. By changing units (e.g. from measuring distance in kilometers to measuring distance in nanometers) you can change the scaling of a variable arbitrarily, so why would you even consider removing low-variance variables?
Can a variable with zero variance be dropped?
Variables which are all 0’s or have near to zero variance can be dropped due to less predictive power. In some cases it might cause a problem as well. Like in Naive Bayes Classifier, if one value is 0, then the entire equation becomes 0.
When to remove correlated variables from data science stack?
If you retrain the model, the opposite could have happened. You should consider checking VIF (Variance Inflation Factor). Try removing features with higher VIF. Generally, it is preferred that VIF is below 10.