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@TobeT I'll try and tackle a few questions.
"in the Prediction Explanations, how do i know what blue (X-Axis 0-0.05) and what red (0.5-1) is in terms of my target?"
- By default, the colored regions correspond to the top and bottom 10% of the prediction distribution. You can adjust those sliders to whatever you prefer or is relevant for your business case.
"DR wouldn't let me saying "Series "Production Date (Month)" contains multiple rows with the same timestamp"
- There are a few things to consider:
- Do you want to use time to help you predict what is going on right now? Or do you want to predict what will happen in the future?
- Today => use OTV (Out of time validation). Having duplicate date-time data will not be a problem
- Future => use Time Series Modeling.
- It sounds like you have multiple time-series sets within a dataset and have duplicate time-date values. If you change the toggle to Multiple Series ("Yes, there are multiple series"), then DataRobot will automatically detect ways it could group the data that will allow use in a single time-series project. If DataRobot doesn't detect a feature it can use to separate your data, then you may need to think about creating a feature that allows you to do so. What category would you like to track over time? Do you need to break your dataset into smaller projects?
"My main goal is for DR to work out what problem the customers have. So far i got really good results, even on multi-variate when I trained DR with 1000 of the 5000 complaints that i have 'binned' to problem groups myself.
What i found is that DR doesn't give me feature insights at all if I have more than two targets, so now I'm going through my problem grouping 1 by 1 eg. Broken Screen = True or False. Then using the same data with target 'Broken Battery' = True or False. "
- Given your level of R or Python programming ability, using one of our APIs would make the model building process for the numerous binary classification models extremely fast. This is especially helpful when you are considering building dozens or more projects to model individual components for a single Machine Learning use case. Both APIs have very thorough documentation.
Hi Thomas,
You have good instincts converting your multiclass project into multiple binary classification ones. You can get more insights when you narrow the focus of the model to looking at each class independently.
The feature effects graph uses a model agnostic approach called partial dependence. This is calculated by basically taking the feature of interest - in the case below "number of inpatient visits", and calculate what the average target probability would be if every row in the dataset had the same number/category.
For example, below the platform first calculates the average probability of being readmitted if everyone in the dataset has the value = 0 for the "number of inpatient visits" and it plots the result. Next it will do the same procedure but will change all of the rows for that feature to = 1 for the "number of inpatient visits" ...and then it will do the same calculation with all rows using =2 as the "number of inpatient visits"....and so on until the whole range is plotted. In this case there were 11 values.
When completed, this graph tells you that as the "number of inpatient visits" increases, so does the likelihood of being readmitted.
More concretely on the plot below, if everyone in the dataset has a 0 for the "number of inpatient visits" then their average probability to be readmitted (keeping everything else the same) is ~36%. However, if everyone has a 5 for the "number of inpatient visits"(everything else kept the same) then their average probability to be readmitted is ~58%. - which indicates that the "number of inpatient visits" increases with the likelihood to be readmitted.
Does this answer your question?
Emily
@TobeT It sounds like you are interested in understanding the impact of time-dependent 'trends' in your data. DataRobot can definitely show you those features if you are using the AutoTS product and data from the period of periodicity or seasonality that you expect is within your Feature Derivation Window, the settings used to determine how far back to create lagged values.
There are several very easy ways to see this in the platform, and are consistent with what @emily and @duncanrenfrow mentioned above. They discussed one evaluation of 'trend' (a changing moving average), but I'm unclear if you are looking for 'trends' or 'patterns'. The below discusses 'patterns' associated with seasonality.
First, we can see the important and/or time-dependent trends in a model-agnostic fashion under the 'Data' tab, here looking at the 'Derived Modeling Data'. You see the features ranked by 'Importance' with their correlation to the target we are trying to predict - in this case 'Sales'. As you see below, some variant of weekly seasonality is very prominent in this data.
Next, if you look at the 'Feature Importance' for a time-series model, you can see these effects on both the 'Original Features' and 'Derived Features' tabs. From the 'Original Features' tab, you can see this by hovering over a feature. You'll see a pop-up window that breaks down the importance of all variants of the feature (in this case 'Sales'). Below, we see that '7 day average baseline' is the most important 'Sales' feature by a large margin.
Finally, you can also see this by looking at the 'Derived Features' tab. Here you see all features ranked independently - not grouped like above. In the image below, you see that 'Sales (7 day average baseline)' is by far the most important feature for predicting future sales. As before, you see that many of the other important features are showing 7, 14, or 28 day seasonality - all variants of weekly seasonality.
Do you have other questions about this?
