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How can you rank the features importance?
You can calculate feature importance by measures such as permutation or shap impact. For each model with computed feature importance, get the ranking of the features. Compute the median rank of each feature by aggregating the ranks of the features across all models. Sort the aggregated list by the computed median rank.
How do you find the p-value for 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 is significance level in machine learning?
Level of significance: Refers to the degree of significance in which we accept or reject the null-hypothesis. 100% accuracy is not possible for accepting or rejecting a hypothesis, so we therefore select a level of significance that is usually 5%.
How do you find the p-value in machine learning?
Take a sample, get the statistic (mean), and work out how likely it is to get such figures if H0 is true. The parameter which tells us the former is the p-value. If the p-value is quite small, H0 is probably wrong. The lower the p-value, the more evidence we have that the null hypothesis is false.
Can your p-value be 0?
In reality, p value can never be zero. Any data collected for some study are certain to be suffered from error at least due to chance (random) cause. Accordingly, for any set of data, it is certain not to obtain “0” p value. However, p value can be very small in some cases.
What does p-value predict?
“a p-value is the probability under a specified statistical model that a statistical summary of the data (e.g., the sample mean difference between two compared groups) would be equal to or more extreme than its observed value.”
How to rank the features, now rank again?
Scikit-Learn, for instance, has SelectPercentile and SelectKBest that select features according to a percentile of the highest scores and according to the k highest scores, respectively. Scores, or weights, is a measure of importance. Examples are p-values from statistical tests and slopes (coefficients) from linear regression equations.
How is the statistical importance of a final ranking determined?
In this case, the statistical importance of the final ranking can be examined by a two-step statistical test. This is a non-parametric procedure consisting of the Friedman test with a corresponding post-hoc test, the Nemenyi test. Both of them are based on average ranks.
How is the importance of a feature calculated?
Most importance scores are calculated by a predictive model that has been fit on the dataset. Inspecting the importance score provides insight into that specific model and which features are the most important and least important to the model when making a prediction.
Which is the best method to find the final ranking?
The method I recommend is based on average ranks, i.e., the first proposal of Peter. In this case, the statistical importance of the final ranking can be examined by a two-step statistical test. This is a non-parametric procedure consisting of the Friedman test with a corresponding post-hoc test, the Nemenyi test.