  Yarbus' claim to decode the observer's task from eye movements has received
mixed reactions. In this paper, we have supported the hypothesis that it is
possible to decode the task. We conducted an exploratory analysis on the
dataset by projecting features and data points into a scatter plot to visualize
the nuance properties for each task. Following this analysis, we eliminated
highly correlated features before training an SVM and Ada Boosting classifier
to predict the tasks from this filtered eye movements data. We achieve an
accuracy of 95.4% on this task classification problem and hence, support the
hypothesis that task classification is possible from a user's eye movement
data.
