Contents
Why do we use oversampling?
Oversampling is capable of improving resolution and signal-to-noise ratio, and can be helpful in avoiding aliasing and phase distortion by relaxing anti-aliasing filter performance requirements. A signal is said to be oversampled by a factor of N if it is sampled at N times the Nyquist rate.
What does Bias do in logistic regression?
Bucketing and Prediction Bias That is, prediction bias for logistic regression only makes sense when grouping enough examples together to be able to compare a predicted value (for example, 0.392) to observed values (for example, 0.394).
What is the outcome of logistic regression?
Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative distribution function of logistic distribution.
Why don’t we use MSE in logistic regression?
The main reason not to use the MSE as the cost function for logistic regression is because you don’t want your cost function to be non-convex in nature. If the cost function is not convex then its difficult for the function to optimally converge.
When to use oversampling in logistic regression?
When modeling rare events with logistic regression, oversampling is a common method to reduce computation complexity (i.e., keep all the rare positive cases but just a subsample of negative cases).
How does oversampling affect the slopes and intercepts?
Effect of oversampling Oversampling does not effect the slopes (parameter estimates), but it effects the intercepts (make it too high). In other words, parameter estimates remain same after sampling but intercepts increases very much after sampling.
How does oversampling affect the predicted probabilities?
Predicted probabilities are affected as it is calculated taking both paramter estimates and intercept (incorrect intercept as stated above). It increases after sampling as intercept is overestimated. Oversampling does not affect sensitivity or specificity measures but false positive and negative rates are affected.
What can you do with a logistic regression model?
Logistic regression gives us a mathematical model that we can we use to estimate the probability of someone volunteering given certain independent variables. The model that logistic regression gives us is usually presented in a table of results with lots of numbers.