It might not appear full of zeros at first sight, but when you put a large data set into a structure suitable for analysis, one characteristic of the new data is that it shows a lot more zeros than ones.
Not all variables in a data set or questions in a survey are equal when it comes to data analysis and analytics. Some variables (questions if it’s a survey) will be inherently better at classifying outcomes than others. For example, if you are using a data set to build a narrative around a particular binary behavior (i.e. people who do X vs people who don’t do X) then there are some considerations about which variables will give you a short cut to the story.
The first rule of thumb is to start with binary predictors, i.e. variables with only two different responses / values. Variables with a greater number of possible responses/values will be more likely to have spurious relationships with the variable that you are trying to predict. Predictors with two levels are less likely to suffer this phenomena.
The second rule of thumb is to select those binary variables that have a similar distribution to the variable that you are trying to predict. For example if you are trying to predict a behavior that has 20% incidence among a certain population then the best predictors to use should also have a 20% / 80% spread across two values.
The reason for this condition being optimal is easily explained. The best predictor is one that identifies all cases correctly. Imagine that the best predictor has two possible values with 40% of cases at a value of 1 and 60% of the cases have a value of 2 in this variable. With this distribution, if 1s are predictive of the behavior we are modelling then only half the 1s can be correctly predictive if the behavior has a 20% incidence. The other half of the 1s are incorrectly predictive. However, if the best predictor had 15% of cases that were 1s and 85% cases had a value of 2 then all the 1s could be correctly predictive. This would be a much better predictor to use – in part because the incidence of 1s (or 2s for that matter) is close to the incidence of behavior we are predicting – meaning that 1s have a better chance of being better predictors.
I have a nice graph to show this too. Watch this space!
I have spent a lot of time thinking about data and data structures. What I have learnt is that there are two types of data structures; data which has only one row per user (e.g survey data) and data which has one row for each unique user event (i.e. click stream data from an app or website) and multiple rows for any user.
Many web-based analytics platforms, like Amazon’s own ML platform, only let its users upload data that has a simple data structure (one row per user such as survey data and customer profile data). Very few platforms allow users to upload event-type data and engineer it into a simple form that can be used in predictive analytics.
Transforming event data requires data engineering and this process can be daunting. To develop Knowledge Leaps further, we have spent a lot of time looking at a wide range of event-type data use cases. Our aim has to been to create a systematic, easy-to-use (given the task) approach to simplifying the data engineering work flow. As with our models, we also want our user interface and processes to be human-readable too.
In our latest release we are launching the Data Processor module. The design of this module has drawn heavily on working with real-world event data. This new feature allows the platform to take in any data type and perform simple processing rules to create analytics-ready data sets in minutes.