hello everybody i am shweta narkar and on behalf of our team i will
be presenting our prototype the model line upper the model LineUpper is an attempt at
enhancing the explainability in automated machine learning systems and applications
majority of the existing automl systems focus on procedural transparency
where the system searches through the algorithmic and optimization spaces
such kind of systems tend to focus on accuracy measures very few systems also focus on having
algorithmic transparency which helps in understanding the working of the auto ml
and how it derives to an answer thus going beyond performance metrics is inevitable
research groups and scientists have made great
attempts at adding explainability to automated machine learning systems
we studied the literature and have combined some of these existing approaches in our prototype
this brings us to the model LineUpper this is our attempt at enhancing explainability
and at the same time providing a global as well as local granularity to the user in understanding the
results of the system here you can see a collage overview of our different modules the metrics
table to your top left the feature importance is to your bottom left and the probability scatter
matrix plot to your right the metrics table helps for the process of elimination and adds a
vote of confidence to the thought process of data scientists using ibm's automl system we generated
four flavors for each algorithm that you see on your screen with iterative modifications the first
algorithm was run as is in the second iteration we performed hyper parameter optimization
in the third round we apply feature engineering and train the estimator on hyper parameters from
algorithm 2 and for the last round we ran hyper parameter optimization again after algorithm 3.
the probability scatter plot matrix gives a pairwise comparison of all the models
that have been selected by the user this helps in identifying different instances
of potential interests verification of decisions and understanding the distribution of data points
for example consider the random forest 2 versus lgbm2 plot on the bottom left
in the fourth quadrant of this graph are cases where lgbm2 is correct but random for s2 is wrong
the feature importance comparison view provides two views we will first look at the global view
which gives us the impact of each feature on the predictions made by each model consider
how each of the algorithms are treating the turn differently
versus how they are treating the fico range high differently
the second view is the local feature importance which gives you the mean local feature
contribution of the points selected by using the brushing feature this helps in understanding
the model's prediction for a specific instance based on how the model made the instances feature
our user study was done on 14 data scientists with varied experiences in the field from the
user study we obtained the following results you can see the different ratings on the system
usability scale and the study also highlighted for us the issues that the participants faced while
interacting with our two some users mentioned confusion about the naming of transformed features
and some would have liked to see more raw data and statistics in the scatter plot matrix
the user study and exit interviews gave us the overarching design recommendations
it is necessary to provide various comparative measures in an interactive and speedy manner
data analysis results and statistics within the system to understand the data in depth
while working will help visualizations that group algorithms by their base algorithms will help in
understanding the impact of optimization and when seeing a sub-optimal feature importance value of a
preferred model a participant expressed interest in tweaking how automl does feature engineering
suggesting a holistic understanding of algorithmic and procedural operations could enhance user
control of automl in conclusion we have set off in the right direction and taken a positive step
towards enhancing explainability in automated machine learning systems we would like everyone
to take away from this the importance of human in the loop and the implications of such an ecosystem
in different fields from finance to health care to disaster management and as for the need of
the field the model LineUpper can be extended to support multi-class classification and regression