true or false

${?}_{{?}_{7.}\text{T}\text{h}\text{e}\text{p}\text{r}\text{o}\text{b}\text{l}\text{e}\text{m}-\text{s}\text{o}\text{l}\text{v}\text{i}\text{n}\text{g}\text{a}\text{g}\text{e}\text{n}\text{t}\text{w}\text{i}\text{t}\text{h}\text{s}\text{e}\text{v}\text{e}\text{r}\text{a}\text{l}\text{i}\text{m}\text{m}\text{e}\text{d}\text{i}\text{a}\text{t}\text{e}\text{o}\text{p}\text{t}\text{i}\text{o}\text{n}\text{s}of\text{u}\text{n}\text{k}\text{n}\text{o}\text{w}\text{n}\text{v}\text{a}\text{l}\text{u}\text{e}\text{c}\text{a}\text{n}\text{d}\text{e}\text{c}\text{i}\text{d}\text{e}\text{w}\text{h}\text{a}\text{t}to}$

$doby$ 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.