In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows,

The yam is an important food crop, especially in the so-called ‘yam zone’ of West Africa: total world production is about 19 million tonnes per annum, some 70 per cent of it grown in Nigeria. Although regarded as mainly a source of carbohydrate, some species are nearly as rich in protein as rice or maize. The increasing acceptability of smaller tubers provides an opportunity for extending the area of cultivation but much more research is still needed on methods of propagation, disease control, and post-harvest storage.

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The Vector Error Correction (VEC) model was used to assess the impact of monetary policy rate on commodity prices in Ghana. Monthly data on monetary policy rate, commodity prices of cocoa, gold and crude oil from January 2005 to December 2017 obtained from the Bank of Ghana was used for the study. The estimated VEC model aided in establishing long and short run relationships between monetary policy rates and the major commodity prices in Ghana. The study revealed that in the long run, monetary policy rates are negatively correlated to crude oil prices, positively correlated to both cocoa prices and gold prices but to a little extent. It was also evident from the study that in the short run, the first lag of monetary policy rate is negatively related to itself and the second lag of monetary policy rate is positively related to itself. Additionally, the first and second lagged periods of cocoa price have positive influence on monetary policy rate in the short run, but the first and second lagged periods of gold price have negative influence on monetary policy rates in the short run. The Granger causality test also reveals that the movement of cocoa prices in Ghana can be explained to cause the movement of monetary policy rates and gold prices in short run. A positive shock in monetary policy rate will have a positive and persistent effect on itself. Likewise, positive shock in monetary policy rate will have a positive and persistent effect on cocoa prices. The response generated from a positive shock on monetary policy rate has a persistent and decreasing effect on both crude oil and gold prices.