Using any of the three classifiers described, and any features you can think of, build the best name gender classifier you can. Begin by splitting the Names Corpus into three subsets: 500 words for the test set, 500 words for the dev-test set, and the remaining 6900 words for the training set. Then, starting with the example name gender classifier, make incremental improvements. Use the dev-test set to check your progress. Once you are satisfied with your classifier, check its final performance on the test set. How does the performance on the test set compare to the performance on the dev-test set? Is this what you'd expect?

3. ranga ramasesh is the operations manager for a firm that is trying to decide which one of four countries it should research for possible outsourcing providers. the first step is to select a country based on cultural risk factors, which are critical to eventual business success with the provider. ranga has reviewed outsourcing provider directories and found that the four countries in the table that follows have an ample number of providers from which they can choose. to aid in the country selection step, he has enlisted the aid of a cultural expert, john wang, who has provided ratings of the various criteria in the table. the resulting ratings are on a 1 to 10 scale, where 1 is a low risk and 10 is a high risk. john has also determined six criteria weightins: trust, with a weight of 0.3; quality, with 0.2; religious, with 0.1; individualism, with 0.2; time, with 0.1; and uncertainity, with 0.1. using the factor-rating method, which country should ranga select? why? (2 points)