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Machine Learning: \u003cbr /\u003e\n\n![alt text](https://github.com/saman-nia/MultiClass-Classification/blob/master/data/Result.png) \u003cbr /\u003e\n\nHere, you can see the performance of deep multi class classification: \u003cbr /\u003e\n\n![alt text](https://github.com/saman-nia/Deep-Learning-MultiClass-Classification/blob/master/data/Image_Performance.png) \u003cbr /\u003e\n\nConcept from: https://developers.google.com/machine-learning/crash-course/multi-class-neural-networks/one-vs-all \u003cbr /\u003e\nOne vs. all provides a way to leverage binary classification. Given a classification problem with N possible solutions, a one-vs.-all solution consists of N separate binary classifiers—one binary classifier for each possible outcome. During training, the model runs through a sequence of binary classifiers, training each to answer a separate classification question. For example, given a picture of a dog, five different recognizers might be trained, four seeing the image as a negative example (not a dog) and one seeing the image as a positive example (a dog). That is: \u003cbr /\u003e\n\nIs this image an apple? No. \u003cbr /\u003e\nIs this image a bear? No. \u003cbr /\u003e\nIs this image candy? No. \u003cbr /\u003e\nIs this image a dog? Yes. \u003cbr /\u003e\nIs this image an egg? No. \u003cbr /\u003e\n\nThis approach is fairly reasonable when the total number of classes is small, but becomes increasingly inefficient as the number of classes rises. \u003cbr /\u003e\n\nWe can create a significantly more efficient one-vs.-all model with a deep neural network in which each output node represents a different class. The following figure suggests this approach: \u003cbr /\u003e\n\n![alt text](https://developers.google.com/machine-learning/crash-course/images/OneVsAll.svg)\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaman-nia%2Fmulticlass-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaman-nia%2Fmulticlass-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaman-nia%2Fmulticlass-classification/lists"}