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the goal of any model is to explain a dependent variable by several independent variables sometimes called predictors but which predictors are useful and how many predictors should we include in our model is usually unknown these questions are important because if we take too many predictors weamp;#39;ll overfit the model and explain the noise in the data instead of uncovering true relationships while if we include only a few predictors into our model will underfit the model and probably miss some potentially important relationships thus we need to find the best model with an optimal set of predictors which explains maximum of our dependent variable without explaining the noise one of the most common solutions for finding the best model is a stepwise variable selection but itamp;#39;s not the best solution out there and here is boy stepwise selection applies two main techniques forwards and backward selection but there are two problems with it first forwards and backwards approaches
