In the related formula I exactly dont understand what the first term is. I know fm(x) is the weak learner but I dont have any idea about ziita.
Related
I'm working on a simple project in which I'm trying to describe the relationship between two positively correlated variables and determine if that relationship is changing over time, and if so, to what degree. I feel like this is something people probably do pretty often, but maybe I'm just not using the correct terminology because google isn't helping me very much.
I've plotted the variables on a scatter plot and know how to determine the correlation coefficient and plot a linear regression. I thought this may be a good first step because the linear regression tells me what I can expect y to be for a given x value. This means I can quantify how "far away" each data point is from the regression line (I think this is called the squared error?). Now I'd like to see what the error looks like for each data point over time. For example, if I have 100 data points and the most recent 20 are much farther away from where the regression line/function says it should be, maybe I could say that the relationship between the variables is showing signs of changing? Does that make any sense at all or am I way off base?
I have a suspicion that there is a much simpler way to do this and/or that I'm going about it in the wrong way. I'd appreciate any guidance you can offer!
I can suggest two strands of literature that study changing relationships over time. Typing these names into google should provide you with a large number of references so I'll stick to more concise descriptions.
(1) Structural break modelling. As the name suggest, this assumes that there has been a sudden change in parameters (e.g. a correlation coefficient). This is applicable if there has been a policy change, change in measurement device, etc. The estimation approach is indeed very close to the procedure you suggest. Namely, you would estimate the squared error (or some other measure of fit) on the full sample and the two sub-samples (before and after break). If the gains in fit are large when dividing the sample, then you would favour the model with the break and use different coefficients before and after the structural change.
(2) Time-varying coefficient models. This approach is more subtle as coefficients will now evolve more slowly over time. These changes can originate from the time evolution of some observed variables or they can be modeled through some unobserved latent process. In the latter case the estimation typically involves the use of state-space models (and thus the Kalman filter or some more advanced filtering techniques).
I hope this helps!
I have faced with a problem which I have no clue even to find a proper keyword to search. So I ask a question here to expect even some keyword or tag.
The background is very complex. But the result I wanna achieve can be described as a simple scene.
Suppose I have a cube made of glass. The cube is full of sponge. And there's a person in the sponge. Now the person does some movement or action. Then of course the sponge is deformed. This person is described as a geometry. I know the person's original pose, which means I know the original geometry. And I also know the deformed geometry. I prefer to describe the sponge as points or grids in the cube. I know that finite element method can do this accurately. But Is there any interpolation method to calculate how the sponge's points will be?
I donot expect any accurate deformation. I just expect that some falloff to show the pinch or stretch.
Any keyword are welcome. Thx so much.
'cause the structure of my scene is fixed, I choose simple KNN to implement this feature. As structure is fixed, I create a kdtree at the very beginning. Then deform other points based on KNN.
I'm trying to solve a question from a Chinese "linear statistical models",
and the chapter containing this question is about weighted least squares.
The question and the way I solve it are as following:
As you can see, the predicted values is very different to the actual value, so I wonder about whether I solve it right or not.
Could somebody tell me what is wrong with it?
And if there are mistakes how I correct it?
The predicted values are actually not that far off from the actual values. This seems fine and a seems like a sensible result here
I'm trying to understand how the feature importance is calculated for regression trees (and their ensemble counterparts). I'm looking at the source code for the function compute_feature_importances in /sklearn/tree/_tree.pyx and cannot quite follow the logic - and there is no reference.
Sorry this may be a very basic question, but I couldn't find a good literature reference for this, and I was hoping someone could either point me in the right direction, or quickly explain the code so I can keep digging.
Thanks
The reference is in the docs rather than the code:
`feature_importances_` : array of shape = [n_features]
The feature importances. The higher, the more important the
feature. The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance [4]_.
.. [4] L. Breiman, and A. Cutler, "Random Forests",
http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
Does anyone can give an example of a SVM? Especially how to get the w and b from a training set?
I tried to search in the internet but it only gives me a large amount of abstract mathematics.
As I am not good at it, so could anyone give me an illustration of a SVM with an example in very details?
Thank you so much.
This diagram on wikipedia provides a good example of what the goal is, but in truth a support vector machine is a lot of complicated math. You find the values for w and b by optimizing a quadratic programming system, and when hidden behind vector mathematics, it's not entirely clear what's going on unless you're well-tuned to the math.