What is the P value How does it help in feature selection?
How does p-value help in feature selection? Removal of different features from the dataset will have different effects on the p-value for the dataset. We can remove different features and measure the p-value in each case. These measured p-values can be used to decide whether to keep a feature or not.
What do you mean by t-test?
A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. A t-test looks at the t-statistic, the t-distribution values, and the degrees of freedom to determine the statistical significance.
Why are t-test and Z-test so important?
The confidence interval, t-test, and z-test are very popular and widely used methods in inferential statistics. They are so important because, for any research or data analysis, we can only use a sample to come to a conclusion about a large population.
Which is better sklearn or F-test for feature selection?
Advantage of using mutual information over F-Test is, it does well with the non-linear relationship between feature and target variable. Sklearn offers feature selection with Mutual Information for regression and classification tasks. F-Test captures the linear relationship well.
Can a training set be used for feature selection?
Secondly, if only Training Set is used for feature selection, then the test set may contain certain set of instances that defies/contradicts the feature selection done only on the Training Set as the overall historical data is not analyzed.
How are feature selection techniques used in statistics?
There are two popular feature selection techniques that can be used for numerical input data and a numerical target variable. Correlation Statistics. Mutual Information Statistics. Let’s take a closer look at each in turn. Correlation is a measure of how two variables change together.