What is KS statistic used for?
In statistics, the Kolmogorov–Smirnov test (K–S test or KS test) is a nonparametric test of the equality of continuous (or discontinuous, see Section 2.2), one-dimensional probability distributions that can be used to compare a sample with a reference probability distribution (one-sample K–S test), or to compare two …
What does the KS test tell us?
From Wikipedia: “The Kolmogorov–Smirnov statistic quantifies a distance between the empirical distribution function of the sample and the cumulative distribution function of the reference distribution, or between the empirical distribution functions of two samples.” KS Test can detect the variance.
How do you know if a distribution is close to normal?
In order to be considered a normal distribution, a data set (when graphed) must follow a bell-shaped symmetrical curve centered around the mean. It must also adhere to the empirical rule that indicates the percentage of the data set that falls within (plus or minus) 1, 2 and 3 standard deviations of the mean.
What does the cut off mean in binary classification?
The Threshold or Cut-off represents in a binary classification the probability that the prediction is true. It represents the tradeoff between false positives and false negatives.
When to choose the probability cut-off in classification problem?
Many a times you may want to choose probability that gives you the maximum accuracy. However, care should be taken when you have a case where the response column is skewed. For instance, a bank wants to predict the loan defaulters.
What does threshold mean in binary classification problem?
The Threshold or Cut-off represents in a Data Mining – (two class|binary) classification problem (yes/no, false/true) the probability that the prediction is true. It represents the tradeoff between false positive and false negative.
Why does error tend to be low in binary classification?
Since we have true negatives in the denominator, our error will tend to be low just because the dataset is imbalanced. Obviously, if we increase the threshold only higher scored observations will be classified as positive.