let' ignore most of the output we got here and focus on the most important partsmodel fit--for this the -squared or adjusted -squared is used this measure is an indication of the amount of variation in the data that gets captured by the model the difference between the adjusted -squared and the -squared is minimal here because the adjusted one is the normal one penalty for model complexity model gets complex when many variables (or featuresare introduced you don' need complex model if simple model is availableso the adjusted -squared punishes you for overcomplicating at any rate is highand it should be because we cheated rules of thumb existbut for models in businessesmodels above are often considered good if you want to win competition you need in the high for research howeveroften very low model fits (< evenare found what' more important there is the influence of the introduced predictor variables predictor variables have coefficient--for linear model this is easy to interpret in our example if you add " to it will change by " it' easy to see how finding good predictor can be your route to nobel prize even though your model as whole is rubbish iffor instanceyou determine that certain gene is significant as cause for cancerthis is important knowledgeeven if that gene in itself doesn' determine whether person will get cancer the example here is classificationnot regressionbut the point remains the samedetecting influences is more important in scientific studies than perfectly fitting models (not to mention more realisticbut when do we know gene has that impactthis is called significance predictor significance--coefficients are greatbut sometimes not enough evidence exists to show that the influence is there this is what the -value is about long explanation about type and type mistakes is possible here but the short explanations would beif the -value is lower than the variable is considered significant for most people in truththis is an arbitrary number it means there' chance the predictor doesn' have any influence do you accept this chance to be wrongthat' up to you several people introduced the extremely significant ( < and marginally significant thresholds ( < linear regression works if you want to predict valuebut what if you want to classify somethingthen you go to classification modelsthe best known among them being -nearest neighbors as shown in figure -nearest neighbors looks at labeled points nearby an unlabeled point andbased on thismakes prediction of what the label should be