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Lung cancer detection is highly challenging as it is asymptomatic till advanced stage. Early lung cancer detection helps to increase the patient's survival. Computer Aided Diagnosis (CAD) systems have been developed using Machine Learning (ML) and Artificial Intelligence (AI) techniques in detecting malicious regions from medical images. This study is intended to compare the classical ML techniques for lung cancer classification from Positron Emission Tomography/Computed Tomography (PET/CT) images. Significant texture and fractal descriptors extracted from PET/CT images generate non-linear data and were fed as inputs to the classifier. Various hyper-parameters and model parameters for the ML techniques have been tuned to fix optimal parameters for better performance. 10-fold cross validation was used to analyze the performance of the classifiers. Experimental study showed that Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel of width, σ = 1 outperformed and achieved highest accuracy of 98.10%.