Two hundred labeled samples are used to train two binary classifiers M$1$ and

M$2.$ For classifier M$1,$ the dataset is divided into training and validation

sets of $100$ samples each and the classifier is trained on the training set. The

performance of M$1$ on this validation set provides a $80\%$ accuracy. For classifier

M$2,$ the dataset is divided into a training set of $150$ samples and a validation set

of $50$ samples, and the classifier is trained on the training set. The performance

of M$2$ on the corresponding validation set provides and accuracy of $90\%.$ Is

classifier M$2$ to be preferred to classifier M$1?$ Justify your answer.