Question $1\mathrm{.}1$: use the one$-$hot$-$encoding technique, transform the above dataset into a dataset

appropriate for classification models to classify whether a player has a shoe sponsor or not. Show

the first three data samples $($IDs $1,2,$ and $3)$ of the above dataset expressed in that format.

Question $1\mathrm{.}2$: given the multiple bar plots above, is feature CAREER STAGE informative for

classifying whether the player has a SHOE SPONSOR or not?

Question $1\mathrm{.}3$: given the multiple bar plots above, is the feature POSITION informative for

classifying whether the player has a SHOE SPONSOR or not?CSC $480/$CSC $680$ Section $002$: Fall $2024$

Midterm Study Guide and Practice Questions

In the data exploration phase, you could use bar plots to $(1)$ visualize the distribution of the feature values in the entire dataset, and $(2)$ illustrates the relationship between two features or a feature and the target. Given small multiple bar plots, know how to analyze the strength of the relationship.

Practice: the dataset below shows the details of thirty players in a professional basketball team. The dataset includes the POSITION that the player normally plays $($guard$,$ center, or forward$),$ the HEIGHT, and WEIGHT, the CAREER STAGE of the player $($rookie$,$ midcareer, or veteran; and AGE of each player; the average weekly SPONSORSHIP EARNINGS of each player; and whether the player has a SHOE SPONSOR $($yes or no$).$

$\backslash$table$[[$ID$,$PoSITION,HEIGHT,WEIGHT,$\backslash$table$[[$CAREER$],[$STAGE$]],$AGE,$\backslash$table$[[$SPONSORSHIP$],[$EARNINGS$]],$SHOE$],[1,$forward,$192,218,$veteran,$29,561,$yes$],[2,$center,$218,251,$mid$-$career,$35,60,$no$],[3,$forward,$197,221,$rookie,$22,1,312,$no$],[4,$forward,$192,219,$rookie,$22,1,359,$no$],[5,$forward,$198,223,$veteran,$29,362,$yes$],[6,$guard,$166,188,$rookie,$21,1,536,$yes$],[7,$forward,$195,221,$veteran,$25,694,$no$],[8,$guard,$182,199,$rookie,$21,1,678,$yes$],[9,$guard,$189,199,$mid$-$career,$27,385,$yes$],[10,$forward,$205,232,$rookie,$24,1,416,$no$],[11,$center,$206,246,$mid$-$career,$29,314,$no$],[12,$guard,$185,207,$rookie,$23,1,497,$yes$],[13,$guard,$172,183,$rookie,$24,1,383,$yes$],[14,$guard,$169,183,$rookie,$24,1,034,$yes$],[15,$guard,$185,197,$mid$-$career,$29,178,$yes$],[16,$forward,$215,232,$mid$-$career,$30,434,$no$],[17,$guard,$158,184,$veteran,$29,162,$yes$],[18,$guard,$190,207,$mid$-$career,$27,648,$yes$],[19,$center,$195,235,$mid$-$career,$28,481,$no$],[20,$guard,$192,200,$mid$-$career,$32,427,$yes$],[21,$forward,$202,220,$mid$-$career,$31,542,$no$],[22,$forward,$184,213,$mid$-$career,$32,12,$no$],[23,$forward,$190,215,$rookie,$22,1,179,$no$],[24,$guard,$178,193,$rookie,$21,1,078,$no$],[25,$guard,$185,200,$mid$-$career,$31,213,$yes$],[26,$forward,$191,218,$rookie,$19,1,855,$no$],[27,$center,$196,235,$veteran,$32,47,$no$],[28,$forward,$198,221,$rookie,$22,1,409,$no$],[29,$center,$207,247,$veteran,$27,1,065,$no$],[30,$center,$201,244,$mid$-$career,$25,1,111,$yes$],[,,,,,,,]]$

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