Contents
How to increase the model accuracy of logistic regression?
1. Feature Scaling and/or Normalization – Check the scales of your gre and gpa features. They differ on 2 orders of magnitude. Therefore, your gre feature will end up dominating the others in a classifier like Logistic Regression.
Can You normalize logistic regression in scikit-learn?
Therefore, your gre feature will end up dominating the others in a classifier like Logistic Regression. You can normalize all your features to the same scale before putting them in a machine learning model. This is a good guide on the various feature scaling and normalization classes available in scikit-learn.
What is the accuracy of a trivial model?
The accuracy of a trivial model that just guesses the modal class for every case would be 10/ (10 + 1) = 91%, which is pretty high. In order to substantially beat 91%, as with 95% accuracy, you need one or more highly predictive features.
How can I improve the accuracy of my model?
Hyperparameter Tuning – Grid Search – You can improve your accuracy by performing a Grid Search to tune the hyperparameters of your model. For example in case of LogisticRegression, the parameter C is a hyperparameter. Also, you should avoid using the test data during grid search.
How to tune a scikit-learn logistic regression model?
While we have managed to improve the base model, there are still many ways to tune the model including polynomial feature generation, sklearn feature selection, and tuning of more hyperparameters for grid search. These will be the focus of Part 2! In the meantime, thanks for reading and the code can be found here.
How are labels assigned in logistic regression model tuning?
For label encoding, a different number is assigned to each unique value in the feature column. A potential issue with this method would be the assumption that the label sizes represent ordinality (i.e. a label of 3 is greater than a label of 1). For one hot encoding, a new feature column is created for each unique value in the feature column.
How are feature elimination methods used in logistic regression?
This would be by coefficient values, recursive feature elimination (RFE) and sci-kit Learn’s SelectFromModels (SFM). All of these methods were applied to the sklearn.linear_model.LogisticRegression since RFE and SFM are both sklearn packages as well.
How to increase accuracy of logistic regression in scikit Python?
Hyperparameter Tuning – Grid Search – You can improve your accuracy by performing a Grid Search to tune the hyperparameters of your model. For example in case of LogisticRegression, the parameter C is a hyperparameter. Also, you should avoid using the test data during grid search. Instead perform cross validation.
How is AUC used to validate logistic regression?
A measure that is often used to validate logistic regression, is the AUC of the ROC curve (plot of sensitivity against 1-specificity – just google for the terms if needed). This, in essence, evaluates the whole range of threshold values.