  Technologies that function at room temperature often require magnets with a
high Curie temperature, $T_\mathrm{C}$, and can be improved with better
materials. Discovering magnetic materials with a substantial $T_\mathrm{C}$ is
challenging because of the large number of candidates and the cost of
fabricating and testing them. Using the two largest known data sets of
experimental Curie temperatures, we develop machine-learning models to make
rapid $T_\mathrm{C}$ predictions solely based on the chemical composition of a
material. We train a random forest model and a $k$-NN one and predict on an
initial dataset of over 2,500 materials and then validate the model on a new
dataset containing over 3,000 entries. The accuracy is compared for multiple
compounds' representations ("descriptors") and regression approaches. A random
forest model provides the most accurate predictions and is not improved by
dimensionality reduction or by using more complex descriptors based on atomic
properties. A random forest model trained on a combination of both datasets
shows that cobalt-rich and iron-rich materials have the highest Curie
temperatures for all binary and ternary compounds. An analysis of the model
reveals systematic error that causes the model to over-predict
low-$T_\mathrm{C}$ materials and under-predict high-$T_\mathrm{C}$ materials.
For exhaustive searches to find new high-$T_\mathrm{C}$ materials, analysis of
the learning rate suggests either that much more data is needed or that more
efficient descriptors are necessary.
