I have a regression problem to solve and in order to find a suitable model, I use the following method:
1°) run a CV for each model with default param
2°) Compare mean(MSE), STD(MSE) and calculation ...
Let's assume I observe a soccer player and I want to model the change in his normal scoring pattern based on the time in minutes he takes to score a goal.
da <- data.frame(game = c(1,1,1,2,2,2,2,3,...
I have an application which uses a dataframe, whose columns of interest are pm2.5 values and date values. I have over 43.000 values registered in the dataframe and the goal is to use the polynomial ...
I'm running a random forest regression using sklearn in Python to predict fantasy points scored for basketball players. I encode the data with pd.get_dummies() so that each player has a column marked ...
I'm using LASSO as a variable selection method for my analysis, but there's one particular variable that I wish to ensure is contained in the final formula. I have automated the entire process to ...
I wanted to derive gradient descent from scratch using the error function for Orthogonal linear regression. So that I could write python code in NumPy.
However, I am unsure about the error function in ...
I'm using GridSearchCV in order to determine the best neighbors for Knn.
As I'm dealing with a regression problem I set scoring to 'r2'.
I don't understand why when using using the training ...
The "leafblotch" data shows the percentage leaf area affected by leaf blotch on 10
varieties of barley at nine different sites.(in Faraway)
A better variance function is $µ^2
(1 − µ)^2$ and yet this ...
I wish to implement a Deep Q-Learning network, and for that I need the last layer of a ActivationNetwork to not have a SigmoidActivation, but a Linear one, because the output is not constrained. I ...
I made a polynomial regression and I want to plot the data, but plotly doesn't plot it properly. What can I do? Here is the code. I think that the data format is somehow not supported by plotly, but i ...
So as the title suggests, my question is whether feature selection algorithms are independent of the regression/classification model chosen. Maybe some feature selection algorithms are independent and ...
I am building an application in Python which can predict the values for Pm2.5 pollution from a dataframe. I am using the values for November and I am trying to first build the linear regression model. ...
I wonder if is there any function or typing in R to check if the effect of a variable on another is significant (True or False; p-value) in a model. I know you can just look at the coefficients, but ...
I'm trying to compare which feature selection model is more eficiente for a specific domain. Nowadays the state of the art in this domain (GWAS) is regression-based algorithms (LR, LMM, SAIGE, etc), ...
I was planning on doing boosted regression trees with random effects and using the package mvtboost to achieve this, but it was removed from CRAN.
https://github.com/patr1ckm/mvtboost
https://cran.r-...