8 ms·

> This process is typically called “feature engineering”, and is part art and part science. Choices on including or excluding certain variables, and how they ar

> This process is typically called “feature engineering”, and is part art and part science. Choices on including or excluding certain variables, and how they are translated into numerical parameters, can significantly impact the model’s performance. According to this article, to make good predictive/regression model, we need a good artist and a good engineer!

rdli 3y ago
(Author here). Or a lot of trial & error :).
- Vaslo 3y ago
  My job is now primarily Time Series Forecasting, and we’ve spent so much time improving our feature selection and engineering. When I started I thought “run correlations against target variables, find the best bunch and as long as we can explain them and their relation to the target we are good” I was wrong.
  - eyegor 3y ago
    Wait I still do this, what are your secrets?
  - bigger_cheese 3y ago
    I work mostly with regressions and often it is almost more informative when something you expected to be a significant term isn't. Can help track down interesting behavior. More recently Machine learning has really enhanced what you can do with regression. For example multivariate regressions when there are non-linear (or partially linear) relationships between feature and target variables. For example recent regression problem involved a chemical reaction. It was suspected that a particular feature above a threshold began to display non linear behavior but it was difficult to pinpoint exactly where it began departing from linearity. ML was very helpful analyzing this. Other than regressions and timeseries forecasting I think it's worth knowing about K-means clustering and PCA (Principal Component Analysis)/ PLS (Projection to latent structures) as well. I've found PCA to be pretty unknown but very useful I've had success using it in the past and found it useful to explain the relationship not just between the data features and the target variable but also how the features relate to each other.
    - applied_heat 3y ago
      I’m just about to start digging in to 8 years of data from a few power plants with 16 turbines in total to see if I can identify some problems we might have before the sensor measurements exceed the alarm threshold. Taking bearing temperature as an example, I think I will identify periods of time where the machine has already been generating for an hour so temperature have stabilized and then I will have bearing oil inlet temperature and machine load as independent variables, and bearing oil outlet and bearing metal temperatures as dependent values. Seems like it should be straightforward to find any anomalies but I’ve just started googling how to do this yesterday. There are lots of vendors hawking predictive maintenance software but I can’t imagine that I couldn’t get similar results with a few weeks effort and armed with Python and all of the associated libraries
      - bigger_cheese 3y ago
        Maybe try slopes and second derivatives (change in temperature over time and so forth) could also try introducing various lag windows into timeseries data. edit: I've Also seen a lot of pitches about predictive maintenance / automated anomaly detection. I think the appeal lies in having a one size fits all solution you can apply to multiple pieces of equipment (fans, conveyor belt drives, pumps etc) and not needing to develop/deploy/maintain bespoke models. A lot of manufacturing sites won't have a data person on tap (or even people who can write python). Also there are challenges with deployment etc especially in remote sites where access is difficult, data connectivity is bad etc (think like oil/gas pipelines). Most of the pitches seem to combine running ML models and using some kind of iot device with something like lorawan for connectivity..
        applied_heat 3y ago
        Is the product being sold the setup of the bespoke models from their bag of what they’ve done before? Regressions seem like the obvious way to detect anomalies to me since it should be 100% repeatable and make sense according to amount of heat being generated and removed , how to apply ML/AI to it I am not so sure
      - 3y ago

rdli 3y ago

(Author here). Or a lot of trial & error :).

Vaslo 3y ago
My job is now primarily Time Series Forecasting, and we’ve spent so much time improving our feature selection and engineering. When I started I thought “run correlations against target variables, find the best bunch and as long as we can explain them and their relation to the target we are good” I was wrong.
- eyegor 3y ago
  Wait I still do this, what are your secrets?
- bigger_cheese 3y ago
  I work mostly with regressions and often it is almost more informative when something you expected to be a significant term isn't. Can help track down interesting behavior. More recently Machine learning has really enhanced what you can do with regression. For example multivariate regressions when there are non-linear (or partially linear) relationships between feature and target variables. For example recent regression problem involved a chemical reaction. It was suspected that a particular feature above a threshold began to display non linear behavior but it was difficult to pinpoint exactly where it began departing from linearity. ML was very helpful analyzing this. Other than regressions and timeseries forecasting I think it's worth knowing about K-means clustering and PCA (Principal Component Analysis)/ PLS (Projection to latent structures) as well. I've found PCA to be pretty unknown but very useful I've had success using it in the past and found it useful to explain the relationship not just between the data features and the target variable but also how the features relate to each other.
  - applied_heat 3y ago
    I’m just about to start digging in to 8 years of data from a few power plants with 16 turbines in total to see if I can identify some problems we might have before the sensor measurements exceed the alarm threshold. Taking bearing temperature as an example, I think I will identify periods of time where the machine has already been generating for an hour so temperature have stabilized and then I will have bearing oil inlet temperature and machine load as independent variables, and bearing oil outlet and bearing metal temperatures as dependent values. Seems like it should be straightforward to find any anomalies but I’ve just started googling how to do this yesterday. There are lots of vendors hawking predictive maintenance software but I can’t imagine that I couldn’t get similar results with a few weeks effort and armed with Python and all of the associated libraries
    - bigger_cheese 3y ago
      Maybe try slopes and second derivatives (change in temperature over time and so forth) could also try introducing various lag windows into timeseries data. edit: I've Also seen a lot of pitches about predictive maintenance / automated anomaly detection. I think the appeal lies in having a one size fits all solution you can apply to multiple pieces of equipment (fans, conveyor belt drives, pumps etc) and not needing to develop/deploy/maintain bespoke models. A lot of manufacturing sites won't have a data person on tap (or even people who can write python). Also there are challenges with deployment etc especially in remote sites where access is difficult, data connectivity is bad etc (think like oil/gas pipelines). Most of the pitches seem to combine running ML models and using some kind of iot device with something like lorawan for connectivity..
      - applied_heat 3y ago
        Is the product being sold the setup of the bespoke models from their bag of what they’ve done before? Regressions seem like the obvious way to detect anomalies to me since it should be 100% repeatable and make sense according to amount of heat being generated and removed , how to apply ML/AI to it I am not so sure
    - 3y ago