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$doby$ just examining different possible sequences $of$ actions that lead $to$ states $of$ known value,

and then choosing the best sequence. This process $of$ looking for such a sequence $is$ called

Search.

${?}_{{?}_{?}}{?}_{{?}_{?}}{?}_{{?}_{?}}8.$ Breadth$-$first search $is$ not optimal when all step costs are equal, because $it$ always expands the

shallowest unexpanded node.

${?}_{{?}_{?}}{?}_{{?}_{?}}{?}_{{?}_{?}}9.InSVM$ model, normalization's main benefit $isto$ avoid having attributes $in$ greater numeric

ranges dominate those $in$ smaller numeric ranges.

${?}_{{?}_{?}}{?}_{{?}_{?}}{?}_{{?}_{?}}10.$ Linear Regression aims $to$ capture the functional relationships between one $or$ more numeric

input variables and a categorical output variable.

${?}_{{?}_{?}}{?}_{{?}_{?}}{?}_{{?}_{?}}11.In$ prediction, linear regression uses a mathematical equation $to$ identify additive mathematical

relationships between explanatory variables and the response variable.

${?}_{{?}_{?}}{?}_{{?}_{?}}{?}_{{?}_{?}}12$ Logistic regression $is$ like linear regression where both $of$ them are used $to$ predict a numeric

target variable.