使用回归而不是分类进行多 class 分类

Question

I have a multi class classification problem. I am using random forest classifier. My boss has asked if it is possible to also view our problem with regression. I understand that for a classification task, it is of course better to use a classifier, but is it possible to implement a regression model.

My data is as such:

I have a dataset consisting of software requirements, these are rated as either 1, 2, 3, 4 or 5.

I am then creating a feature matrix to use for training the model to make predictions on the class, with 10 features such as: num_words, num_sentences, num_syllables, weak_words, flesh_idx etc

My model works quite well with 93% accuracy.

Is there a way I can view this problem using regression? Such that the model would make predictions such as 1.5 for example, where the prediction doesn't fall into the class 1 or 2 but somewhere in the middle? Or maybe 2.2, 3.3 etc as opposed to 1, 2, 3, 4, or 5?

I guess the reason is just to see if we can see the software requirement scores in a continuous way.

Answer 1

try Softmax regression (or multinomial logistic regression) with mxnet or with tensorflow

Answer 2

The way you can use regression in classification problems is with Logistic Regressions. You can use this individually to classify 1 vs not 1, 2 vs not 2, and so on for each classification (don't do this), or use Softmax that in simple words, weights each class and returns a probability for each given class, then you just pick the one with the max probability and that will be your predicted class. There are a lot of neural networks that use softmax when working with mutli-class classification. Here is a great article from scikit-learn's documentation: https://scikit-learn.org/stable/modules/neural_networks_supervised.html