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Confusion Matrix in Machine Learning

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  Confusion Matrix in Machine Learning The confusion matrix is a matrix used to determine the performance of the classification models for a given set of test data. It can only be determined if the true values for test data are known. The matrix itself can be easily understood, but the related terminologies may be confusing. Since it shows the errors in the model performance in the form of a matrix, hence also known as an  error matrix . Some features of Confusion matrix are given below: For the 2 prediction classes of classifiers, the matrix is of 2*2 table, for 3 classes, it is 3*3 table, and so on. The matrix is divided into two dimensions, that are  predicted values  and  actual values  along with the total number of predictions. Predicted values are those values, which are predicted by the model, and actual values are the true values for the given observations. It looks like the below table: The above table has the following cases: True Negative:  Model has given prediction No, an
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Machine Learning aand its types

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Introduction to Machine Learning: Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for   building mathematical models and making predictions using historical data or information . Currently, it is being used for various tasks such as   image recognition ,   speech recognition ,   email filtering ,   Facebook auto-tagging ,   recommender system , and many more. This machine learning tutorial gives you an introduction to machine learning along with the wide range of machine learning techniques such as  Supervised ,  Unsupervised , and  Reinforcement  learning. In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. So here comes the role of  Machine Learning . Machine Learning is said as a subset of  artificial intelligence  that is mainly concerned with the
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