# a baseline ML model

I do not know how to interpret the concept of a baseline ML model.

"Before spending months cleaning data, establish exactly what you want to use that data for, and establish a baseline ML model to guide you on your cleaning journey."

Does it mean, that at the beginning the data should be collected, lightly pre-processed (set up the correct types for variables, impute missing data, remove outliers, normalization), run several algorithms at once and check the performance? Build and train a basic system quickly? Do Max 2 days work?

What if the result is ca. R=20%?

Would be such a model as a baseline accepted?

• What exactly is your question? – Amit Keinan 2 days ago
• Are you sure $R^2 = 0.2$ is so awful? – Dave 2 days ago

When you calculate $$R^2$$ for a linear regression, you are comparing your model to a baseline model that always guesses the mean of the observed $$y$$ values. The $$SSRes$$ is the numerator measures how much error (variance) your model has, and the $$SSTot$$ in the denominator measures how much error (variance) the naïve model that always achieves the pooled mean of your data has.
$$R^2 = 1- \dfrac{SSRes}{SSTot} = \dfrac{\dfrac{\sum(y_i - \hat{y_i})^2}{n-1}}{\dfrac{\sum(y_i - \bar{y})^2}{n-1}}$$
(The $$n-1$$ denominators, of course, cancel out to give the more familiar equation, but this representation, I think, ties it back to variance.)
The $$\frac{\sum(y_i - \bar{y})^2}{n-1}$$ is the same as $$\frac{\sum(y_i - \hat{y_i})^2}{n-1}$$ if every $$\hat{y_i} = \bar{y}$$, in other words, always guessing the average value of $$y$$.