title: What's a base model and why/when do we go for other ML algorithms
body: <p>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).</p>
  <p>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 <em>base model</em> 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?</p>
  <p>The other question is what do they gain from presenting the other algorithms in their research papers if these algorithms performed poorly?</p>