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pommedeterresautee
2015-03-01 13:01:42 +01:00
parent 8e52c4b45a
commit 46082a54c9
3 changed files with 47 additions and 39 deletions
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R-package/vignettes/xgboostPresentation.Rmd
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@@ -204,7 +204,7 @@ pred <- predict(bst, test$data)
print(length(pred))
# limit display of predictions to the first 10
print(pred[1:10])
print(head(pred))
```
These numbers doesn't look like *binary classification* `{0,1}`. We need to perform a simple transformation before being able to use these results.
@@ -220,7 +220,7 @@ If we think about the meaning of a regression applied to our data, the numbers w
```{r predictingTest, message=F, warning=F}
prediction <- as.numeric(pred > 0.5)
print(prediction[1:10])
print(head(prediction))
```
Measuring model performance
@@ -241,7 +241,7 @@ Steps explanation:
2. `probabilityVectorPreviouslyComputed != test$label` computes the vector of error between true data and computed probabilities ;
3. `mean(vectorOfErrors)` computes the *average error* itself.
The most important thing to remember is that **to do a classification, you just do a regression to the `label` and then apply a threeshold**.
The most important thing to remember is that **to do a classification, you just do a regression to the** `label` **and then apply a threeshold**.
*Multiclass* classification works in a similar way.