Let's assume we're dealing with continuous features and responses. We fit a linear regression model (let's say first order) and after CV we get a somehow good r^2 (let's say r^2=0.8).

Why do we go for other ML algorithms? I've read some research papers and they were trying different ML algorithms and taking the simple linear model as a base model for comparison. In these papers, the linear model outperformed other algorithms, what I have difficulty understanding is why do we go for other ML algorithms then? Why can't we just be satisfied with the linear model especially in the specific case where other algorithms perform poorly?

The other question is what do they gain from presenting the other algorithms in their research papers if these algorithms performed poorly?

CodePudding user response：

The Best model for solving predictive problems such as continuous output is the regression model especially if you do it using a Neural network (polynomial or linear) with hyperparameter tuning based on the problem.

Using other ML algorithms such as Decision Tree or SVM or any other model where their main goal is classification but on the paper, they say it can do regression also in fact, they can't predict any new values.

but in the field of research people always try to find a better way to predict values other than regression, like in the classification world we start with Logistic regression -> decision tree and now we have SVM and ensembling models and DeepLearning.

CodePudding user response：

I think the answer is because you never know.

especially in the specific case where other algorithms perform poorly?

You know they performed poorly because someone tried dose models. It's always worthy trying various models.