Determine whether the statement is true or false and provide a brief explanation for your

decision. You may receive no credit for a correct answer with no explanation.

$1\mathrm{.}1$ The k$-$nearest neighbour $($k$-$NN$)$ algorithm typically involves more computation during

train time compared to test time. $[1]$

$1\mathrm{.}2$ If you train a linear regression estimator with only half the data, the predictive performance of the model will be better. $[1]$

$1\mathrm{.}3$ Splitting in decision tree is the process of dividing a node into two or more sub$-$nodes. $[1]$

$1\mathrm{.}4$ Both C$4\mathrm{.}5$ and CART are among the well$-$known decision tree algorithms extensively

used in machine learning to build decision trees. $[1]$

$1\mathrm{.}5$ The C$4\mathrm{.}5$ technique builds a decision tree from the training data in a bottom$-$up$,$

iterative manner. $[1]$