In　this　paper,　the　machine　learning　(ML)　model　is　built　for　slope　stability　evaluation　and　meets　the　high　precision　and　rapidity　requirements　in　slope　engineering.　Different　ML　methods　for　the　factor　of　safety　(FOS)　prediction　are　studied　and　compared　hoping　to　make　the　best　use　of　the　large　variety　of　existing　statistical　and　ML　regression　methods　collected.　The　data　set　of　this　study　includes　six　characteristics,　namely　unit　weight,　cohesion,　internal　friction　angle,　slope　angle,　slope　height,　and　pore　water　pressure　ratio.　The　whole　ML　model　is　primarily　divided　into　data　preprocessing,　outlier　processing,　and　model　evaluation.　In　the　data　preprocessing,　the　duplicated　data　are　first　removed,　then　the　outliers　are　filtered　by　the　LocalOutlierFactor　method　and　finally,　the　data　are　standardized.　11　ML　methods　are　evaluated　for　their　ability　to　learn　the　FOS　based　on　different　input　parameter　combinations.　By　analyzing　the　evaluation　indicators　R-2,　MAE,　and　MSE　of　these　methods,　SVM,　GBR,　and　Bagging　are　considered　to　be　the　best　regression　methods.　The　performance　and　reliability　of　the　nonlinear　regression　method　are　slightly　better　than　that　of　the　linear　regression　method.　Also,　the　SVM-poly　method　is　used　to　analyze　the　susceptibility　of　slope　parameters.