Can we use PCA with logistic regression?

Can we use PCA with logistic regression?

Therefore, PCA can effectively eliminate multicollinearity between features. In this post, we’ll build a logistic regression model on a classification dataset called breast_cancer data.

Can logistic regression be used for both classification and regression tasks?

Logistic regression is a powerful machine learning algorithm that utilizes a sigmoid function and works best on binary classification problems, although it can be used on multi-class classification problems through the “one vs. all” method. Logistic regression (despite its name) is not fit for regression tasks.

Why is logistic regression better for classification?

Logistic regression is a simple yet very powerful algorithm to solve binary classification problems. The logistic function (i.e. sigmoid function) is also commonly used in very complex neural networks as the activation function of output layer.

What’s the difference between a logistic regression and a PCA?

The PCA does an unsupervised dimensionality reduction, while the logistic regression does the prediction.

How to use principal component analysis for logistic regression?

You must do your PCA on the whole data. First combine the samples from both classes. Then split your data in train and test. (train and test sets must contain data from both classes) Apply the PCA transformation you trained in (3) on both train and test set Train and Test your logistic regression model on the projected datasets

Can a PCA be used as a axis transformation?

Yes. PCA is basically a fancy axis transformation. Yes you get a new set of features, but they are linear combinations of the previous features in a ordered way such that the first feature describes as much as possible of the data.

Why is PCA used for linear dimensionality reduction?

The idea is that, if you have a hyperplane, PCA will actually project the hyperplane to the first axes and leave the last ones nearly empty. PCA is linear dimensionality reduction, so if the true data distribution is not linear, it gives worse results.