Welcome to r/learnmachinelearning - a community of learners and educators passionate about machine learning! This is your space to ask questions, share resources, and grow together in understanding ML concepts - from basic principles to advanced techniques. Whether you're writing your first ... More on reddit.com

I think it’s just because it is easier to explain gradient descent with a trivial linear regression example. More on reddit.com

Mixed models are a secret weapon More on reddit.com

Linear regression is a valuable tool, but it's only one of the tools in your toolbox. (Other basic tools are things like XMAs - XMAs of everything, of prices, returns, vol, and even histograms - because things change. Moving up the complexity ladder you have clustering, PCA analysis, RFs, ANNs, MCMC, etc.) You need to understand LR's strengths and weaknesses. It's well-known for example that certain problems, like the XOR problem, are not linearly separable. And so LR, a linear method, won't be able to solve that problem - whereas a multi-layer NN with non-linear activation functions will be able to, as will a RF. On the other hand, LR is very fast to compute, and while you lose on accuracy, you on balance probably gain on robustness. LR models also typically extrapolate more robustly than RF and ANN models - where you would be very careful about allowing the input to take on values outside of the historical range. One huge advantage of LR models is the associated R2 metric, which has a really intuitive and appealing interpretation. Namely, it tells you the proportion of variance in the output variable accounted for by the variance in the input variable. So if R2 is 10%, that means that 10% of the variation in the output - the thing you care about - is "explained" by the variations in the input. Let's take a step back and consider how you even measure if a strategy is good. The really naive way is to run a so-called backtest. Which I'm sure you already know about. But in my experience, that's not how the pros do it. Instead, they'll measure how good a model is by regressing the value of the indicator (let's say 1 means buy, 0 means hold and -1 means sell) against future returns, and you'll typically hope that your model will explain a few percent of the variation in the future returns. (The future returns are called the labels from ML context where the input and output are typically images and textual labels - "person on bike", that kind of thing. But in finance the labels are almost always future returns over some time period.) If your R2 is higher than, say, 10% - then you're kind of kicking ass - and you can be very sure that even a simple trading strategy will work. By the way, that simple strategy will make use of the LR insofar as it will - in the simplified single variable case - multiply the output of your indicator with "slope" in your LR to get as output your expected future return. And now you can say, is that return higher than my commissions? If yes, then if expected return is positive, then buy, else sell. Pushing the boat, let's say you have two or even three models which you, from the above analysis, you think are promising. How do you combine them? Again for simplicity assume each takes on a value in the closed range [-1, 1]. The answer is again LR. Now you're going to take the output signal from each model as the _input_ to another regression, again using the same labels - the future returns. And this will tell you whether the models cooperate - or compete - with each other. And you will know this by seeing if combined you get an improvement in R2. You can also inspect the slopes. You might even regress the parameters of a model, with the parameters of the same model but trained on month more of data. If your model is any good, the parameters should have some kind of stability over time. So your prior months params should predict current params. And finally it's good smoke test for more complicated models. If you find your complicated RF model is doing worse than LR, then you know you need to go back to the drawing board. More on reddit.com