regression coefficient in Dataiku

UserBird Dataiker, Alpha Tester Posts: 535 Dataiker
edited July 16 in Using Dataiku


I am used to analyse R regression coefficients and I am a little bit confused about how to do it in dataiku. For instance on the Iris dataset, If I fit a regression on the iris dataset to explain sepal length with the Species and the Petal length I have :

lm(formula = iris$Sepal.Length ~ iris$Petal.Length + iris$Species)

Min 1Q Median 3Q Max
-0.75310 -0.23142 -0.00081 0.23085 1.03100

Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.68353 0.10610 34.719 < 2e-16 ***
iris$Petal.Length 0.90456 0.06479 13.962 < 2e-16 ***
iris$Speciesversicolor -1.60097 0.19347 -8.275 7.37e-14 ***
iris$Speciesvirginica -2.11767 0.27346 -7.744 1.48e-12 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.338 on 146 degrees of freedom
Multiple R-squared: 0.8367, Adjusted R-squared: 0.8334
F-statistic: 249.4 on 3 and 146 DF, p-value: < 2.2e-16

The two regression coefficients iris$Speciesversicolor, iris$Speciesvirginica are to compared with the Species taken as reference (Setosa). Meaning, that iris$Speciesvirginica is the difference of sepal length in mean between the species virginica and setosa.

In dataiku, I have three coefficient and I don't know what is the reference. Besides, none of my coefficients are significative in dataiku whereas there are all significative in R :

species = Iris-virginica ☆☆☆ 9.01e-21.3485 0.3129
species = Iris-setosa ☆☆☆ 8.16e-2-1.4032- 0.3081
petal_l ☆☆☆ 4.02e-10.2486 0.2450
species = Iris-versicolor ☆☆☆ 4.57e-1-0.1091-0.0233
Intercept 5.8531

Could you explain why?


  • PGuti
    PGuti Registered Posts: 5 ✭✭✭✭✭

    The difference in DSS is that 1 dummy variable is created per category so there is no reference species (that's why you have one coefficient per species). I agree that it is closer to the ML view that the Statistical view.

    The second difference is that in DSS, we always start by creating a train and test set. So you may have train a regression on 80 % of the data in DSS and 100 % using R.

    Does this answer your question ? To explain it further I would need the exact parameters you choose for your regression (exact model, rescaling options, etc ... ).
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