Feature Selection using Fisher Score and Chi2 Test | Titanic Dataset
Download Working Datafile: https://github.com/laxmimerit/Feature-Selection-using-Fisher-Score-and-Chi2-Test-Titanic-Dataset
High-dimensional data in the input space is usually not good for classification due to the curse of dimensionality. It significantly increases the time and space complexity for processing the data. Moreover, in the presence of many irrelevant and/or redundant features, learning methods tend to over-fit and become less interpretable. A common way to resolve this problem is feature selection, which reduces the dimensionality by selecting a subset of features from the input feature set. It is often used to reduce the computational cost and remove irrelevant and redundant features for problems with high dimensional data.
Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which leads to a suboptimal subset of features
#Chi Square Test A chi-squared test, also written as χ2 test, is any statistical hypothesis test where the sampling distribution of the test statistic is a chi-squared distribution.
The chi-squared test is used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories. i.e. It is applied only on categorical dataset
chi-square test measures dependence between stochastic variables, so using this function “weeds out” the features that are the most likely to be independent of class and therefore irrelevant for classification.
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