This machine learning algorithm is based on supervised\nlearning and revolves around the input and output variables using an algorithm\nto predict the outcome. It runs a regression task. It models a target prediction\nvalue based on independent variables, which is mostly used for finding out the\nrelationship between variables & forecasting. <\/p>\n\n\n\n
In this technique, a relationship is established among independent and dependent variables by bringing them to a line. This line is known as the regression line and expressed by a linear equation Y= a*X + b.<\/p>\n\n\n\n
Where: <\/p>\n\n\n\n
Y = Dependent Variable<\/p>\n\n\n\n
a = Slope<\/p>\n\n\n\n
X = Independent variable<\/p>\n\n\n\n
b = Intercept<\/p>\n\n\n\n
We get coefficients ‘a’ & ‘b’ by minimizing the sum of\nthe squared difference of distance between data points and the regression line.<\/p>\n\n\n\n
Logical regression:<\/strong><\/p>\n\n\n\n Identical to linear regression, logical regression is\nanother statistical method for classification which finds the values for two\ncoefficients that weigh each input variable. The difference between the two is\nthat, this solves problems of binary classification, relying on a logical,\nnon-linear function instead. Hence, logical regression determines whether a\ndata instance belongs to one class or another and can also provide the\nreasoning behind the prediction, unlike linear regression.<\/p>\n\n\n\n When using this algorithm, limiting correlating data and\neliminating noise is also necessary.<\/p>\n\n\n\n Classification and\nRegression Trees:<\/strong><\/p>\n\n\n\n Decision Trees are a significant type of algorithm for\npredictive modeling machine learning.<\/p>\n\n\n\n These decision tree algorithms have been around for decades and modern variations like random forest are among the most robust techniques available. Classification and Regression Trees or ‘CART’ is a term introduced by a distinguished statistician Leo Breiman to refer to Decision Tree algorithms that can be used for classification or regression predictive modeling problems. Generally, it is referred to as \u201cdecision trees\u201d, but on few platforms like R they are referred by the more modern term CART. <\/p>\n\n\n\n In this algorithm, we divide the population into two or more\nhomogeneous sets based on the most relevant attributes\/ independent variables.<\/p>\n\n\n\n K-nearest neighbor\n(KNN):<\/strong><\/p>\n\n\n\n KNN is an acronym for the K-nearest neighbor method, in\nwhich the user defines the value of K. Unlike previous algorithms, this one\ntrains on the entire data-set.<\/p>\n\n\n\n The algorithm guides the machine to check the entire\ndata-set to find the k-nearest instances to this new data instance or to find\nthe k-number of instances that are the most closely related to the new\ninstance. The prediction or output can be one of the below two things:<\/p>\n\n\n\n \u2013 The mode of most frequent class, in a classification\nproblem<\/p>\n\n\n\n \u2013 The mean of the outcomes, in a regression problem<\/p>\n\n\n\n This algorithm can be easily understood by comparing it with\nreal life situations, likewise if you want to know about a person, you should\nask about him\/ her from his friends or colleagues.<\/p>\n\n\n\n Na\u00efve Bayes:<\/strong><\/p>\n\n\n\n To calculate the probability that if an event will occur or\nnot, given that another event has already occurred, we use Bayes\u2019s Theorem,\nwhich is: P(h|d)= (P(d|h) P(h)) \/ P(d)<\/p>\n\n\n\n In this equation:<\/p>\n\n\n\n P(h|d) = Posterior probability i.e. the probability of\nhypothesis h being true, given the data d, where P(h|d)= P(d1| h) P(d2|\nh)\u2026.P(dn| h) P(d)<\/p>\n\n\n\n P(d|h) = Likelihood i.e. the probability of data d given\nthat the hypothesis h was true.<\/p>\n\n\n\n P(h) = Class prior probability i.e. the probability of\nhypothesis h being true (irrespective of the data)<\/p>\n\n\n\n P(d) = Predictor prior probability i.e. probability of the\ndata (irrespective of the hypothesis)<\/p>\n\n\n\n This algorithm is known as \u2018naive\u2019 because it assumes that\nall the variables are independent of each other, which is a naive assumption to\nmake in real-world examples.<\/p>\n\n\n\n Conclusion:<\/strong><\/p>\n\n\n\n Applying these five machine learning algorithms may not be\ntoo complicated, but they do take time to master. These are some important\nbuilding blocks that can serve as a solid starting point for further study of\nmore advanced algorithms and methods.<\/p>\n","protected":false},"excerpt":{"rendered":" This machine learning algorithm is based on supervised learning and revolves around the input and output variables using an algorithm to predict the outcome. It runs a regression task. 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