  We explore the use of random forest and gradient boosting, two powerful
tree-based machine learning algorithms, for the detection of cosmic strings in
maps of the cosmic microwave background (CMB), through their unique
Gott-Kaiser-Stebbins effect on the temperature anisotropies.The information in
the maps is compressed into feature vectors before being passed to the learning
units. The feature vectors contain various statistical measures of processed
CMB maps that boost the cosmic string detectability. Our proposed classifiers,
after training, give results improved over or similar to the claimed
detectability levels of the existing methods for string tension, $G\mu$. They
can make $3\sigma$ detection of strings with $G\mu \gtrsim 2.1\times 10^{-10}$
for noise-free, $0.9'$-resolution CMB observations. The minimum detectable
tension increases to $G\mu \gtrsim 3.0\times 10^{-8}$ for a more realistic, CMB
S4-like (II) strategy, still a significant improvement over the previous
results.
