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Merge pull request #193 from pommedeterresautee/master

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Vignette text (very biiiiig change)
This commit is contained in:
Tianqi Chen 2015-03-13 14:50:49 -07:00
commit 90ade3bb84
1 changed files with 5 additions and 5 deletions
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10
R-package/vignettes/discoverYourData.Rmd
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@ -153,7 +153,7 @@ head(sparse_matrix)
Create the output `numeric` vector (not as a sparse `Matrix`):
```{r}
output_vector = df[,Y:=0][Improved == "Marked",Y:=1][,Y]
output_vector = df[,Improved] == "Marked"
```
1. set `Y` vector to `0`;
@ -250,7 +250,7 @@ According to the plot above, the most important features in this dataset to pred
* the Age ;
* having received a placebo or not ;
* the sex is third but already included in the not interesting feature ;
* the sex is third but already included in the not interesting features group ;
* then we see our generated features (AgeDiscret). We can see that their contribution is very low.
Do these results make sense?
@ -261,21 +261,21 @@ Let's check some **Chi2** between each of these features and the label.
Higher **Chi2** means better correlation.
```{r, warning=FALSE, message=FALSE}
c2 <- chisq.test(df$Age, df$Y)
c2 <- chisq.test(df$Age, output_vector)
print(c2)
```
Pearson correlation between Age and illness disapearing is **`r round(c2$statistic, 2 )`**.
```{r, warning=FALSE, message=FALSE}
c2 <- chisq.test(df$AgeDiscret, df$Y)
c2 <- chisq.test(df$AgeDiscret, output_vector)
print(c2)
```
Our first simplification of Age gives a Pearson correlation is **`r round(c2$statistic, 2)`**.
```{r, warning=FALSE, message=FALSE}
c2 <- chisq.test(df$AgeCat, df$Y)
c2 <- chisq.test(df$AgeCat, output_vector)
print(c2)
```