The induction of Artificial intelligence is gradually impacted the recruitment landscape. Dr Nathan Mondragon, Chief Industrial Organisational Psychologist at HireVue compares the bias nature of human against Artificial Intelligence. While we tend to focus on the fears of an autonomous future, there has been little discussion about how machines are actually benefiting industries and augmenting humans. For instance, in the recruitment industry, there has been a significant uptake and investment by companies of all sizes, integrating Artificial Intelligence (AI) and machine learning into the hiring process over the last few years. Companies are employing these solutions in order to save time and money for both themselves and for candidates, as well as to help them widen their talent pool and hire greater and more diverse talent.
One important approach to learning Bayesian networks (BNs) from data uses a scoring metric to evaluate the fitness of any given candidate network for the data base, and applies a search procedure to explore the set of candidate networks. The most usual search methods are greedy hill climbing, either deterministic or stochastic, although other techniques have also been used. In this paper we propose a new algorithm for learning BNs based on a recently introduced metaheuristic, which has been successfully applied to solve a variety of combinatorial optimization problems: ant colony optimization (ACO). We describe all the elements necessary to tackle our learning problem using this metaheuristic, and experimentally compare the performance of our ACO-based algorithm with other algorithms used in the literature. The experimental work is carried out using three different domains: ALARM, INSURANCE and BOBLO.
In machine learning, asking the right question and knowing the correct answer is the most important. We should know what question to ask and this is the most important part of the process. After that, we should ask ourselves whether we have enough and correct data to answer that question. If you ask the wrong question or you do not have enough or correct data, the answer you will get can never be what it should be and what exactly is expected. For example, if we take an example of internet banking transaction frauds, we ask ourselves how we can predict that a transaction is going to be fraudulent.
Things in machine learning are repeated over and over and hence machine learning is iterative in nature. Therefore, to know machine learning, one has to understand the machine learning process. The machine learning process is a bit tricky and challenging. It is very rare that we find the machine learning process easy. The reason for it being so complex is very clear - a large amount of complex data is involved in this process and out of which we try to find out meaningful predictive patterns and model.
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