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How do you solve logistic regression overfitting?
One of the ways to combat over-fitting is to increase the training data size. Let take the case of MNIST data set trained with 5000 and 50000 examples,using similar training process and parameters. we can observe that training and validation errors steadily decrease during the initial part of the learning process.
Is it possible for logistic regression models to overfit?
It is indeed possible to overfit a logistic regression model. Aside from linear dependence (if the model matrix is of deficient rank), you can also have perfect concordance, or that is the plot of fitted values against Y perfectly discriminates cases and controls.
Is it possible to overfit a logistic regression model?
I saw a video saying that if my area under the ROC curve is higher than 95%, then its very likely to be over fitted, but is it possible to overfit a logistic regression model? Yes, you can overfit logistic regression models.
How can you detect an overfitting regression model?
Consequently, you can detect overfitting by determining whether your model fits new data as well as it fits the data used to estimate the model. In statistics, we call this cross-validation, and it often involves partitioning your data.
How to test your understanding of logistic regression?
Suppose you have given the two scatter plot “a” and “b” for two classes ( blue for positive and red for negative class). In scatter plot “a”, you correctly classified all data points using logistic regression ( black line is a decision boundary). Model will become very simple so bias will be very high.
What does overfitting mean in machine learning algorithms?
Overfitting in Machine Learning. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model.