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When to use logistic regression for high dimensional problems?
Therefore, when you apply logistic regression to very high dimensional problems, your fitted probabilities will be closer to 0/1 (because higher variance), and therefore your coefficient estimates must be biased (incorrect) because of this problem? Is this hypothesis correct? I’ve created a simulation with the following code to try to answer this:
How to rank features in a logistic regression model?
After looking into things a little, I came upon three ways to rank features in a Logistic Regression model. This would be by coefficient values, recursive feature elimination (RFE) and sci-kit Learn’s SelectFromModels (SFM).
When to use mL to fit a logistic regression?
The basic intuition behind using ML estimation to fit a logistic regression model is as follows: we seek estimates for β0 β 0 and β1 β 1 such that the predicted probability ˆp(Xi) p ^ ( X i) of attrition for each employee corresponds as closely as possible to the employee’s observed attrition status.
What is the area under the curve score in logistic regression?
The original LogReg function with all features (18 total) resulted in an “area under the curve” (AUC) of 0.9771113517371199 and an F1 score of 93%. Coefficient Ranking: AUC: 0.975317873246652; F1: 93%. Since we did reduce the features by over half, losing .002 is a pretty good result.
Where do I get my logistic regression data?
The data set is taken from the Conway & Myles Machine Learning for Hackers book, Chapter 2, and can it can be directly downloaded here. This is a preview of what the data looks like: Each sample contains three columns: Height, Weight, and Male.
What are the columns in a logistic regression?
Each sample contains three columns: Height, Weight, and Male. Male: 1 means that the measurement corresponds to a male person, and 0 means that the measurement corresponds to a female person. There are 5,000 samples from males, and 5,000 samples for females, thus the data set is balanced and we can proceed to training.
How is logistic regression used in binary classification?
Logistic Regression is a popular statistical model used for binary classification, that is for predictions of the type this or that, yes or no, A or B, etc. Logistic regression can, however, be used for multiclass classification, but here we will focus on its simplest application.