How can I increase my AUC?

How can I increase my AUC?

One possible alternative (depending on your classification technique) is to use class weights instead using sampling techniques. Adding a greater penalty to misclassifying your under represented class can reduce bias without “over training” on the under-represented class samples.

What is a good AUC for logistic regression?

Area Under the ROC curve It can range from 0.5 to 1, and the larger it is the better. People will sometimes use the AUC as a means for evaluating predictive performance of a model, although because it represents all possible cutoff values, which isn’t feasible in practice, the interpretation is difficult.

What does logistic regression maximize?

The parameters of a logistic regression model can be estimated by the probabilistic framework called maximum likelihood estimation. The parameters of the model can be estimated by maximizing a likelihood function that predicts the mean of a Bernoulli distribution for each example.

What does AUC mean in logistic regression?

Area under the curve
AUC stands for Area under the curve. AUC gives the rate of successful classification by the logistic model. The AUC makes it easy to compare the ROC curve of one model to another. The AUC for the red ROC curve is greater than the AUC for the blue ROC curve. This means that the Red curve is better.

What does AUC stand for in logistic regression?

Just by glancing over the graph, we can conclude that threshold C is better than threshold B and depending on how many False Positives that we are willing to accept, we can choose the optimal threshold. AUC stands for Area under the curve. AUC gives the rate of successful classification by the logistic model.

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.

Why are ROC and AUC metrics so important?

If the Red ROC curve was generated by say, a Random Forest and the Blue ROC by Logistic Regression we could conclude that the Random classifier did a better job in classifying the patients. AUC and ROC are important evaluation metrics for calculating the performance of any classification model’s performance.

How to calculate sensitivity of ROC and AUC?

Let’s create a Confusion Matrix to summarize the classifications. Once the confusion matrix is filled in, we can calculate the Sensitivity and the Specificity to evaluate this logistic regression at 0.5 threshold. In the above confusion matrix, let’s replace the numbers with what they actually represent.