We apply four statistical learning methods to a sample of $7941$ galaxies ($z<0.06$) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using $10$ features measured for each galaxy (sizes, colours, shape parameters & stellar mass) we apply the techniques of Support Vector Machines (SVM), Classification Trees (CT), Classification Trees with Random Forest (CTRF) and Neural Networks (NN), returning True Prediction Ratios (TPRs) of $75.8%$, $69.0%$, $76.2%$ and $76.0%$ respectively. Those occasions whereby all four algorithms agree with each other yet disagree with the visual classification (`unanimous disagreement) serves as a potential indicator of human error in classification, occurring in $sim9%$ of ellipticals, $sim9%$ of Little Blue Spheroids, $sim14%$ of early-type spirals, $sim21%$ of intermediate-type spirals and $sim4%$ of late-type spirals & irregulars. We observe that the choice of parameters rather than that of algorithms is more crucial in determining classification accuracy. Due to its simplicity in formulation and implementation, we recommend the CTRF algorithm for classifying future galaxy datasets. Adopting the CTRF algorithm, the TPRs of the 5 galaxy types are : E, $70.1%$; LBS, $75.6%$; S0-Sa, $63.6%$; Sab-Scd, $56.4%$ and Sd-Irr, $88.9%$. Further, we train a binary classifier using this CTRF algorithm that divides galaxies into spheroid-dominated (E, LBS & S0-Sa) and disk-dominated (Sab-Scd & Sd-Irr), achieving an overall accuracy of $89.8%$. This translates into an accuracy of $84.9%$ for spheroid-dominated systems and $92.5%$ for disk-dominated systems.
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