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What is the meaning of Copy_X in sklearn linear models


What is the meaning of Copy_X in sklearn linear models

By : Divya Garg
Date : October 21 2020, 08:10 PM
I hope this helps . On the documentation for Linear Regression, the following is provided:
code :
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.


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glmulti , lmer fit (linear mixed models) and gls fit models(lme package)

glmulti , lmer fit (linear mixed models) and gls fit models(lme package)


By : Pankaj067
Date : March 29 2020, 07:55 AM
Hope this helps Why does functions from the glmulti R package not work well on lmer fit (linear mixed models) and gls fit models(lme package): , This works for me with sessionInfo() as follows:
code :
R Under development (unstable) (2014-09-17 r66626)
Platform: i686-pc-linux-gnu (32-bit)

other attached packages:
[1] glmulti_1.0.7 rJava_0.9-6   lme4_1.1-8    Rcpp_0.11.2   Matrix_1.1-4 

loaded via a namespace (and not attached):
[1] compiler_3.2.0  grid_3.2.0      lattice_0.20-29 MASS_7.3-34    
[5] minqa_1.2.3     nlme_3.1-117    nloptr_1.0.4    splines_3.2.0  
[9] tools_3.2.0  
library("lme4")
library("glmulti")
dd <- read.table("SO_glmulti.dat",header=TRUE)
m1 <- lmer(Yeild~ (TShann+Alt+Slope+CPT+MAT+MARF)^2+
               (1|Blocks)+(1|Composition),
           data=dd)
setMethod('getfit', 'merMod', function(object, ...) {
    summ <- coef(summary(object))
    summ1 <- summ[,1:2,drop=FALSE]
    ## if (length(dimnames(summ)[[1]])==1) {
    ##     summ1 <- matrix(summ1, nr=1,
    ##                     dimnames=list(c("(Intercept)"),
    ##                     c("Estimate","Std. Error")))
    ## }
    cbind(summ1, df=rep(10000,length(fixef(object))))
})
lmer.glmulti<-function(formula,data,random="",...) {
    lmer(paste(deparse(formula),random),data=data,
         REML=FALSE,...)
}
lmer.glmulti<-function(formula,data,random="",...) {
    newf <- formula
    newf[[3]] <- substitute(f+r,
                            list(f=newf[[3]],
                                 r=reformulate(random)[[2]]))
    lmer(newf,data=data,
         REML=FALSE,...)
}
glmulti_lmm <- glmulti(formula(m1,fixed.only=TRUE),
                     random="+(1|Blocks)+(1|Composition)",
                       data=dd,method="g",
                       deltaM=0.5, 
                       fitfunc=lmer.glmulti,
                       intercept=TRUE,marginality=FALSE,level=2)
                  Estimate Uncond. variance Nb models   Importance
[... skip ...]
CPT:MARF      1.334119e-03     5.491836e-07        11 0.7875438701
CPT:MAT       4.051261e-02     5.215084e-04        18 0.7995790960
TShann        1.260082e+00     1.650145e+00        35 0.8111493166
CPT          -9.923303e-01     2.764638e-01        74 0.9600979205
Alt:CPT      -2.465917e-04     7.937155e-09        72 0.9765910742
(Intercept)   3.754893e+01     5.988814e+01       100 1.0000000000
Sklearn - Linear regression

Sklearn - Linear regression


By : serg910
Date : March 29 2020, 07:55 AM
it fixes the issue Your code has error in the constructor of LinearRegression.
Instead of:
code :
reg = LinearRegression(x,y)
reg = LinearRegression()
(n_rows,)
(n_rows, n_columns)
X = X.reshape(-1,1)
Training Linear Models with MAE using sklearn in Python

Training Linear Models with MAE using sklearn in Python


By : Aakar
Date : March 29 2020, 07:55 AM
it helps some times In SGD, if you use 'epsilon_insensitive' with epsilon=0 it should work as if you used MAE.
You could also take a look at statsmodels quantile regression (using MAE is also called median regression, and median is a quantile).
Approach for comparing linear, non-linear and different parameterization non-linear models

Approach for comparing linear, non-linear and different parameterization non-linear models


By : user2590008
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further I search for one approach for comparing linear, non-linear and different parameterization non-linear models. For this:
code :
#First cross-validation approach ------------------------------------------

#Cross-validation model 1
set.seed(123) # for reproducibility

n <- nrow(d)
frac <- 0.8
ix <- sample(n, frac * n) # indexes of in sample rows

e1<- Diameter ~ a1 * Age^a2 
#Algoritm Levenberg-Marquardt
m1 <-  nlsLM(e1, data = d,
     start = list(a1 = 0.1, a2 = 10),
     control = nls.control(maxiter = 1000), subset = ix)# in sample model

BOD.out <- d[-ix, ] # out of sample data
pred <- predict(m1, new = BOD.out)
act <- BOD.out$Diameter
RSS1 <- sum( (pred - act)^2 )
RSS1
#[1] 56435894734

#Cross-validation model 2
m2<-lm(Diameter ~ Age, data=d,, subset = ix)# in sample model
BOD.out2 <- d[-ix, ] # out of sample data
pred <- predict(m2, new = BOD.out2)
act <- BOD.out2$Diameter
RSS2 <- sum( (pred - act)^2 )
RSS2
#[1] 19.11031

# Sum of squares approach -----------------------------------------------
deviance(m1)
#[1] 238314429037

deviance(m2)
#[1] 257.8223
Using sklearn models as input to deep learning models

Using sklearn models as input to deep learning models


By : user3095431
Date : March 29 2020, 07:55 AM
wish help you to fix your issue You should probably explore Stacking : http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/
What happens is that when we are doing cross validation, we can combine combine the out of fold predictions to regenerate the training data.
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