The most intuitive of all ensemble learning methods is majority voting. It is intuitive, as the aim is to output the most popular (or most voted for) of the base learner's predictions. This chapter covers the basic theory as well as practical implementations concerning majority voting. By the end of this chapter, you will be able to do the following:

• Understand majority voting
• Understand the difference between hard and soft majority voting and their respective strengths and weaknesses
• Implement both versions in Python
• Utilize the voting technique to improve the performance of classifiers on the breast cancer dataset