  A machine learning based surrogate model for fishbone linear instability in
tokamaks is constructed. Hybrid simulations with the
kinetic-magnetohydrodynamic (MHD) code M3D-K is used to generate the database
of fishbone linear instability, through scanning the four key parameters which
are thought to determine the fishbone physics. The four key parameters include
(1) central total beta of both thermal plasma and fast ions, (2) the fast ion
pressure fraction, (3) central value of safety factor $q$ and (4) the radius of
$q=1$ surface. Four machine learning methods including linear regression,
support vector machines (SVM) with linear kernel, SVM with nonlinear kernel and
multi-layer perceptron are used to predict the fishbone instability, growth
rate and real frequency, mode structure respectively. Among the four methods,
SVM with nonlinear kernel performs very well to predict the linear instability
with accuracy $\approx$95%, growth rate and real frequency with
$R^2\approx$98%, mode structure with $R^2\approx$98%.
