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(Solved): what are the prior and conditional probabilites and the naive bayes model....in other words, how wer ...
what are the prior and conditional probabilites and the naive bayes model....in other words, how were these values computed? thank you
\begin{tabular}{ccccc} \hline ID & HISTORY & COAPPLICANT & ACCOMMODATION & FRAUD \\ \hline 1 & current & none & own & true \\ 2 & paid & none & own & false \\ 3 & paid & none & own & false \\ 4 & paid & guarantor & rent & true \\ 5 & arrears & none & own & false \\ 6 & arrears & none & own & true \\ 7 & current & none & own & false \\ 8 & arrears & none & own & false \\ 9 & current & none & rent & false \\ 10 & none & none & own & true \\ 11 & current & coapplicant & own & false \\ 12 & current & none & own & true \\ 13 & current & none & rent & true \\ 14 & paid & none & own & false \\ 15 & arrears & none & own & false \\ 16 & current & none & own & false \\ 17 & arrears & coapplicant & rent & false \\ 18 & arrears & none & free & false \\ 19 & arrears & none & own & false \\ 20 & paid & none & own & false \\ \hline \end{tabular} The probabilities needed by a naive Bayes p
P
(
f
r
)
=
0.3
P
(
CH
=
none
∣
f
r
)
=
0.1666
P
(
CH
=
none
∣
¬
f
r
)
=
0
P
(
CH
=
paid
∣
f
r
)
=
0.1666
P
(
CH
=
paid
∣
¬
f
r
)
=
0.2857
P
(
CH
=
current
∣
f
r
)
=
0.5
P
(
CH
=
current
∣
¬
f
r
)
=
0.2857
P
(
CH
=
arrears
∣
f
r
)
=
0.1666
P
(
CH
=
arrears
∣
¬
f
r
)
=
0.4286
P
(
GC
=
none
∣
f
r
)
=
0.8334
P
(
GC
=
none
∣
¬
f
r
)
=
0.8571
P
(
GC
=
guarantor
∣
f
r
)
=
0.1666
P
(
GC
=
guarantor
∣
¬
f
r
)
=
0
P
(
GC
=
coapplicant
∣
f
r
)
=
0
P
(
GC
=
coapplicant
∣
¬
f
r
)
=
0.1429
P
(
ACC
=
own
∣
f
r
)
=
0.6666
P
(
ACC
=
own
∣
¬
f
r
)
=
0.7857
P
(
ACC
=
rent
∣
f
r
)
=
0.3333
P
(
ACC
=
rent
∣
¬
f
r
)
=
0.1429
P
(
ACC
=
free
∣
f
r
)
=
0
P
(
ACC
=
free
∣
¬
f
r
)
=
0.0714
Notation key:
FR
=
FRAUD,
CH
=
CREDIT HISTORY,
GC
=
GUARANTOR/COAPPLICANT, ACC =ACCOMMODATION.
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