1. True or False. Circle the best singie answer for each problem. {a} TRUE ,1\" FALSE : The eld of data mining is reallyr searehing for information, not ata. (b) TRUE ,3 FALSE : 1Pruning is a terlinique for reducing eomplexity. {e} TRUE ,3 FALSE : Unsupervised data mining is easier to evaluate than super- vi ' ata mining. (d) TRUE ,r' FALSE : Information gain measures how much an attribute deereasos entree}.r ue to new information being added. {e} TRUE ,3 FALSE : Farts failure prcxiietion is an example application for data mining. {f} TRUE 3' FALSE : ("entroidhasc elustering is the procedure that all observa tions start in one cluster, and splits are performed reeursivelv as one moves down the hierarchy. {g} TRUE 3' FALSE : The \"Iimeans\" algorithm is an iterative eenterhased algo rit m. (h) TRUE ,3 FALSE : The Hathaway Effeet raises questions of eerrelation versus eansation. {i} TRUE ,3 FALSE : IFlue ean build a classifieatien model by anaiyzing a set of training ata. {j} TRUE ,3 FALSE : llEorrelation implies eansation. {k} TRUE f FALSE : Datadriven deeision making is oorrelated 1with higher return on assets, return on equity, asset utilization, and market value. {I} TRUE f FALSE : When rendueting supervised data mining, the value of the target variable is known when the model is natal