docs/gitbook/binaryclass/a9a_lr.md

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This pages shows an example of applying logistic regression for a9a binary classification task.

> #### Caution
>
> `logloss()` became deprecated since v0.5.0 release. Use smarter [general classifier](./a9a_generic.md) instead.

<!-- toc -->

# UDF preparation

```sql
select count(1) from a9atrain;
-- set total_steps ideally be "count(1) / #map tasks"
set hivevar:total_steps=32561;

select count(1) from a9atest;
set hivevar:num_test_instances=16281;
```

# training

```sql
create table a9a_model1 
as
select 
 cast(feature as int) as feature,
 avg(weight) as weight
from 
 (select 
     logress(add_bias(features),label,"-total_steps ${total_steps}") as (feature,weight)
  from 
     a9atrain
 ) t 
group by feature;
```

> #### Note
>
> `-total_steps` option is optional for logress() function. We recommend you NOT to use options (e.g., `total_steps` and `eta0`) if you are not familiar with those options. Hivemall then uses an autonomic ETA (learning rate) estimator.

# prediction

```sql
create or replace view a9a_predict1 
as
WITH a9atest_exploded as (
select 
  rowid,
  label,
  extract_feature(feature) as feature,
  extract_weight(feature) as value
from 
  a9atest LATERAL VIEW explode(add_bias(features)) t AS feature
)
select
  t.rowid, 
  sigmoid(sum(m.weight * t.value)) as prob,
  CAST((case when sigmoid(sum(m.weight * t.value)) >= 0.5 then 1.0 else 0.0 end) as FLOAT) as label
from 
  a9atest_exploded t LEFT OUTER JOIN
  a9a_model1 m ON (t.feature = m.feature)
group by
  t.rowid;
```

# evaluation

```sql
create or replace view a9a_submit1 as
select 
  t.label as actual, 
  pd.label as predicted, 
  pd.prob as probability
from 
  a9atest t JOIN a9a_predict1 pd 
    on (t.rowid = pd.rowid);
```

```sql
select count(1) / ${num_test_instances} from a9a_submit1 
where actual == predicted;
```

> 0.8430071862907684