regression

问题 I am trying to do time series prediction with tensorflow and keras with X and y having different dimensions: X.shape = (5000, 12) y.shape = (5000, 3, 12) When I do the following n_input = 7 generator = TimeseriesGenerator(X, y, length=n_input, batch_size=1) for i in range(5): x_, y_ = generator[i] print(x_.shape) print(y_.shape) I get as desired the output (1, 7, 12) (1, 3, 12) (1, 7, 12) (1, 3, 12) ... This is because my data is meteorological, I have 5000 days, for training in the array X I

问题 I want to apply k-fold cross validation on the following regression models: Linear Regression Polynomial Regression Support Vector Regression Decision Tree Regression Random Forest Regression I am able to apply k-fold cross validation on all except polynomial regression which gives me this error PolynomialFeatures' object has no attribute 'predict . How to work around this issue. Also am I doing the job correctly, actually my main motive is to see which model is performing better, so is there

问题 I want to apply k-fold cross validation on the following regression models: Linear Regression Polynomial Regression Support Vector Regression Decision Tree Regression Random Forest Regression I am able to apply k-fold cross validation on all except polynomial regression which gives me this error PolynomialFeatures' object has no attribute 'predict . How to work around this issue. Also am I doing the job correctly, actually my main motive is to see which model is performing better, so is there

问题 |Hi guys, I am asking you for help as I am stucked with bootstrapping... The task is: Use the nonparametric bootstrap to compute bootstrap standard error of CAPM beta estimate based on 1000 bootstrap replications and bootstrap sample size equal to the size of the original sample. If I understand it correctly, I am supposed to run my regression model 1000 times to estimate different estimates of the beta and its standard error. However, I am not able to put my thoughts into an actual R code.

问题 I am working with rasters and I've a RasterStack with 7n layers. I would like to calculate pixel-wise regression, using formula beneath. I was trying to do it with raster::calc , but my function failed with message : 'Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases.' But all rasters are OK, and contain numbers (not only NAs), I can plot it, and I can calculate general linear regression with formula cr.sig=lm (raster::as.array(MK_trend.EVI.sig_Only) ~

问题 |Hi guys, I am asking you for help as I am stucked with bootstrapping... The task is: Use the nonparametric bootstrap to compute bootstrap standard error of CAPM beta estimate based on 1000 bootstrap replications and bootstrap sample size equal to the size of the original sample. If I understand it correctly, I am supposed to run my regression model 1000 times to estimate different estimates of the beta and its standard error. However, I am not able to put my thoughts into an actual R code.

问题 |Hi guys, I am asking you for help as I am stucked with bootstrapping... The task is: Use the nonparametric bootstrap to compute bootstrap standard error of CAPM beta estimate based on 1000 bootstrap replications and bootstrap sample size equal to the size of the original sample. If I understand it correctly, I am supposed to run my regression model 1000 times to estimate different estimates of the beta and its standard error. However, I am not able to put my thoughts into an actual R code.

问题 I am working with rasters and I've a RasterStack with 7n layers. I would like to calculate pixel-wise regression, using formula beneath. I was trying to do it with raster::calc , but my function failed with message : 'Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases.' But all rasters are OK, and contain numbers (not only NAs), I can plot it, and I can calculate general linear regression with formula cr.sig=lm (raster::as.array(MK_trend.EVI.sig_Only) ~

问题 As per the the below code for Polynomial Regression coefficients value, when I calculate the regression value at any x point. Value obtained is way more away from the equivalent y coordinate (specially for the below coordinates). Can anyone explain why the difference is so high, can this be minimized or any flaw in understanding. The current requirement is not a difference of more 150 at every point. import numpy as np x=[0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100] y=[0,885

问题 As per the the below code for Polynomial Regression coefficients value, when I calculate the regression value at any x point. Value obtained is way more away from the equivalent y coordinate (specially for the below coordinates). Can anyone explain why the difference is so high, can this be minimized or any flaw in understanding. The current requirement is not a difference of more 150 at every point. import numpy as np x=[0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100] y=[0,885