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Can you please finish these questions before 7:30 pm EASTEARN TIME. I submitted this three hours ago and the tutor is still workking on it....

In a dataset, there are 1000 samples from apples and oranges. The dataset is split into two parts as 90% and 10% for
training and testing, respectively. In the testing data, there are equal numbers of for apples and oranges. A supervised
machine learning algorithm is trained to detect apples and oranges using the training dataset. When it is tested with
the testing data, it finds the following results: Answer with an integer, no spaces.
• It classifies 5 apples as oranges.
• It classifies 40 oranges as oranges.
Fill out the confusion matrix for the given information above:
Model 1
Predicted Class
Apple (+)
Actual Class
Apple (+)
Oranges (-)
a
С
What are the a, b, c, d values in the confusion matrix?
a: ?
b: ?
c:?
d:?
Oranges (-)
b
d
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Transcribed Image Text:In a dataset, there are 1000 samples from apples and oranges. The dataset is split into two parts as 90% and 10% for training and testing, respectively. In the testing data, there are equal numbers of for apples and oranges. A supervised machine learning algorithm is trained to detect apples and oranges using the training dataset. When it is tested with the testing data, it finds the following results: Answer with an integer, no spaces. • It classifies 5 apples as oranges. • It classifies 40 oranges as oranges. Fill out the confusion matrix for the given information above: Model 1 Predicted Class Apple (+) Actual Class Apple (+) Oranges (-) a С What are the a, b, c, d values in the confusion matrix? a: ? b: ? c:? d:? Oranges (-) b d
In CNN, the convolution and pooling layers may change the size of the feature
map. A simple CNN is given below with input image size of 32x32x3. In each layer,
several filters are applied with different filter size (f), stride (s), and padding (p).
Compute the missing values (a, b, c, d, e, f).
a=
b=
POOL1
Max Pool
d filters
OrÖTÖTT?
f=2
s=2 14x14xc s=1
axaxb
p=0
C=
d=
e=
f=
32 x 32 x 3
6 filters
f=5
s=1
CONV1
p=0
f=5
CONV2
p=0
Max Pool
f=2
POOL2
10 x 10 x 16 = 2 exexf
p=0
expand button
Transcribed Image Text:In CNN, the convolution and pooling layers may change the size of the feature map. A simple CNN is given below with input image size of 32x32x3. In each layer, several filters are applied with different filter size (f), stride (s), and padding (p). Compute the missing values (a, b, c, d, e, f). a= b= POOL1 Max Pool d filters OrÖTÖTT? f=2 s=2 14x14xc s=1 axaxb p=0 C= d= e= f= 32 x 32 x 3 6 filters f=5 s=1 CONV1 p=0 f=5 CONV2 p=0 Max Pool f=2 POOL2 10 x 10 x 16 = 2 exexf p=0
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