docs/gitbook/eval/regression.md

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Using the [E2006 tfidf regression example](../regression/e2006_arow.html), we explain how to evaluate the prediction model on Hive.

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# Scoring by evaluation metrics

```sql
select avg(actual), avg(predicted) from e2006tfidf_pa2a_submit;
```
> -3.8200363760415414     -3.9124877451612488

```sql
set hivevar:mean_actual=-3.8200363760415414;

select
-- Root Mean Squared Error
   rmse(predicted, actual) as RMSE,
   -- sqrt(sum(pow(predicted - actual,2.0))/count(1)) as RMSE,
-- Mean Squared Error
   mse(predicted, actual) as MSE,
   -- sum(pow(predicted - actual,2.0))/count(1) as MSE,
-- Mean Absolute Error
   mae(predicted, actual) as MAE,
   -- sum(abs(predicted - actual))/count(1) as MAE,
-- coefficient of determination (R^2)
   -- 1 - sum(pow(actual - predicted,2.0)) / sum(pow(actual - ${mean_actual},2.0)) as R2
   r2(actual, predicted) as R2 -- supported since Hivemall v0.4.1-alpha.5
from
   e2006tfidf_pa2a_submit;
```
> 0.38538660838804495     0.14852283792484033     0.2466732002711477      0.48623913673053565

# Logarithmic Loss

[Logarithmic Loss](https://www.kaggle.com/wiki/LogarithmicLoss) can be computed as follows:

```sql
WITH t as (
  select
    0 as actual,
    0.01 as predicted
  union all
  select
    1 as actual,
    0.02 as predicted
)
select
   -SUM(actual*LN(predicted)+(1-actual)*LN(1-predicted))/count(1) as logloss1,
  logloss(predicted, actual) as logloss2 -- supported since Hivemall v0.4.2-rc.1
from
from t;
```
> 1.9610366706408238	1.9610366706408238

# References

* R2 http://en.wikipedia.org/wiki/Coefficient_of_determination
* Evaluation Metrics https://www.kaggle.com/wiki/Metrics