create a predictive score including significant fields

Question

Hi,
I created a model with a boolean variable goal. The name of the is Class.
After i would like to create a predictive score with additional data that includes the fields that is classified as the most important by Signal.
I did not understand the reason why by creating a score including fields in a first case and excluding them in another the values of model output does not change. I used the full range technique in all case. 
The fields name are: bj_000 and ck_000.
An row example 
case with 2 important fields includes:

bj_000

ck_000

class

class_mo

Feature_1_Name

Feature_1_Weight

Feature_2_Name

Feature_2_Weight

errorMessage

0.3828125

1.0

false

0.33333333333333337

bb_000

0.12

az_000

0.08000000000000002

case without  important fields:

class

class_mo

errorMessage

false

0.33333333333333337

Does anyone have any suggestions?

cmorfin · Accepted Answer

Hi @alessandro96

Thank you to clarifying.

Here are some answers fro you:

- the class_mo is an internal field which you should not worry too much about. The value that you want is the class field. For Boolean goal the _mo field is roughly a value of the likelihood to be false or true. The closer _mo is to 0, the more likely it is to be false. The closer to 1, the more likely it is to be true.
Thingworx Analytics uses a threshold to switch between false and true (it is not always 0.5, but usually close)

- Using different learners will indeed change the score values, this is expected. All learners are different algorithm that try to predict the new score. This is based on statistics, so each algorithm will reach a different value.
This is why a big part of machine learning model creation is about trial to check which algorithm give the best results based on a specific dataset.
As mentioned earlier, using an ensemble technique (like elite average) allow to make it easier because it will average the output of the best algorithm selected. But you can have cases where a single learner will bring better result, so this is why testing is required.

- To score new data, if you do it through Builder UI, the easiest is to add a new column beforehand in your dataset to use as a filter. For the original data, you can set a value of training.

Then when you add new data, you can set it to scoring04032019 and score only those filtered rows
This is explained at https://community.ptc.com/t5/IoT-Tech-Tips/How-to-score-new-data-with-ThingWorx-Analytics-8-3-x/m-p/571051 (check time 2:05 in the video).

If you use the services of the predictionThing, instead of Builder, then you can specify the new scoring dataset directly to the score job, without creating filters.

Hope this helps

Kind regards

Christophe

cmorfin · Answer

Hi @alessandro96

When you perform a scoring with a request for important feature, this does not change the scoring result (the prediction). What it does is , in addition to the actual score, output the x number of most important feature that impact that score. So if you ask for 2 important features, for each record you will have the actual score and the 2 feature with their weight that impacted the most the prediction for that specific record.
In the example you submitted, the 2 most important features for this specific record are bb_000 and az_000.

See also Causal scoring at https://support.ptc.com/help/thingworx_hc/thingworx_analytics_8/#page/analytics%2Ftwxa-thingpredictor.html

Hope this helps

Kind regards

Christophe

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