recombines these features to achieve its predictions often you may need to consult an expert or the appropriate literature to come up with meaningful features certain features are the variables you get from data setas is the case with the provided data sets in our exercises and in most school exercises in practice you'll need to find the features yourselfwhich may be scattered among different data sets in several projects we had to bring together more than different data sources before we had the raw data we required often you'll need to apply transformation to an input before it becomes good predictor or to combine multiple inputs an example of combining multiple inputs would be interaction variablesthe impact of either single variable is lowbut if both are present their impact becomes immense this is especially true in chemical and medical environments for examplealthough vinegar and bleach are fairly harmless common household products by themselvesmixing them results in poisonous chlorine gasa gas that killed thousands during world war in medicineclinical pharmacy is discipline dedicated to researching the effect of the interaction of medicines this is an important joband it doesn' even have to involve two medicines to produce potentially dangerous results for examplemixing an antifungal medicine such as sporanox with grapefruit has serious side effects sometimes you have to use modeling techniques to derive featuresthe output of model becomes part of another model this isn' uncommonespecially in text mining documents can first be annotated to classify the content into categoriesor you can count the number of geographic places or persons in the text this counting is often more difficult than it soundsmodels are first applied to recognize certain words as person or place all this new information is then poured into the model you want to build one of the biggest mistakes in model construction is the availability biasyour features are only the ones that you could easily get your hands on and your model consequently represents this one-sided "truth models suffering from availability bias often fail when they're validated because it becomes clear that they're not valid representation of the truth in world war iiafter bombing runs on german territorymany of the english planes came back with bullet holes in the wingsaround the noseand near the tail of the plane almost none of them had bullet holes in the cockpittail rudderor engine blockso engineering decided extra armor plating should be added to the wings this looked like sound idea until mathematician by the name of abraham wald explained the obviousness of their mistakethey only took into account the planes that returned the bullet holes on the wings were actually the least of their concernbecause at least plane with this kind of damage could make it back home for repairs plane fortification was hence increased on the spots that were unscathed on returning planes the initial reasoning suffered from availability biasthe engineers ignored an important part of the data because it was harder to obtain in this case they were luckybecause the reasoning could be reversed to get the intended result without getting the data from the crashed planes when the initial features are createda model can be trained to the data