algorithms (including some real-world applicationsbefore doing the same for unsupervised algorithms linear methods the most basic supervised learning algorithms model simple linear relationship between the input features and the output variable that we wish to predict linear regression the simplest of all the algorithms is linear regressionwhich uses model that assumes linear relationship between the input variables (xand the single output variable (yif the true relationship between the inputs and the output is linear and the input variables are not highly correlated ( situation known as collinearity)linear regression may be an appropriate choice if the true relationship is more complex or nonlinearlinear regression will underfit the data because it is so simpleinterpreting the relationship modeled by the algorithm is also very straightforward interpretability is very important consideration for applied machine learning because solutions need to be understood and enacted by both technical and nontechnical people in industry without interpretabilitythe solutions become inscrutable black boxes strengths linear regression is simpleintrepretableand hard to overfit because it cannot model overly complex relationships it is an excellent choice when the underlying relationship between the input and output variables is linear weaknesses linear regression will underfit the data when the relationship between the input and output variables is nonlinear applications since the true underlying relationship between human weight and human height is linearlinear regression is great for predicting weight using height as the input variable orvice versafor predicting height using weight as the input variable this list is by no means exhaustive but does include the most commonly used machine learning algorithms there may be other potential issues that might make linear regression poor choiceincluding outlierscorrelation of error termsand nonconstant variance of error terms closer look at supervised algorithms