  We utilize machine learning to study the string landscape. Deep data dives
and conjecture generation are proposed as useful frameworks for utilizing
machine learning in the landscape, and examples of each are presented. A
decision tree accurately predicts the number of weak Fano toric threefolds
arising from reflexive polytopes, each of which determines a smooth F-theory
compactification, and linear regression generates a previously proven
conjecture for the gauge group rank in an ensemble of $\frac43 \times 2.96
\times 10^{755}$ F-theory compactifications. Logistic regression generates a
new conjecture for when $E_6$ arises in the large ensemble of F-theory
compactifications, which is then rigorously proven. This result may be relevant
for the appearance of visible sectors in the ensemble. Through conjecture
generation, machine learning is useful not only for numerics, but also for
rigorous results.
