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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 (+) Apple (+) Oranges (-) What are the a, b, c, d values in the confusion matrix? a: ? b: ? c:? d:? Actual Class a С Oranges (-) b d

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 (+) Apple (+) Oranges (-) What are the a, b, c, d values in the confusion matrix? a: ? b: ? c:? d:? Actual Class a С Oranges (-) b d

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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
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
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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