uilding models before we get on with the process of building modelswe should try to understand what model represents in the simplest of termsa model can be described as relationship between output or response variables and its corresponding input or independent variables in dataset sometimes this relationship can just be among input variables (in case of datasets with no defined output or dependent variablesthis relationship among variables can be expressed in terms of mathematical equationsfunctionsand ruleswhich link the output of the model to its inputs consider the case of linear regression analysisthe output in that case is set of parameters also known as weights or coefficients (we explore this later in the and those parameters define the relationship between the input and output variables the idea is to build model using learning processsuch that you can learn the necessary parameters (coefficientsin the model that help translate the input variables (independentinto the corresponding output variable (dependentwith the least error for dataset (leveraging validation metrics like mean squared errorthe idea is not to predict correct output value for every input data point (leads to model over-fittingbut to generalize well over lots of data points such that the error is minimum and the same is maintained when you use this model over new data points this is done by learning the right values of coefficients\parameters during the model building process so when we say we are learning linear regression modelthese are the set of important considerations implicit in that statement see figure - figure - high-level representation of model building when we specify linear regression as the candidate modelwe define the nature of relationship between our dependent and independent variables the candidate model then becomes all the possible combinations of parameters for our model (more on this laterthe learning algorithm is the way to determine the most optimal value of those parameters using some optimization process and validating the performance with some metrics (such as mean squared error to reduce the overall errorthe final model is nothing but most optimal value of our parameters as selected by our learning algorithm so in the case of simple linear regression is nothing but tuple containing the values of our two parametersa and point to remember here is that the term parameter is analogous to coefficients or weights in model there are some other types of parameters called hyperparameterswhich represent higher-level meta-parameters of the model and do not depend on the underlying data they usually need to be set before we start the