Classification and CNN Quiz

Which metric is also known as sensitivity?

• Precision

• Recall

• F1 Score

• Accuracy

What does the F1 Score measure?

• The ratio of true positives to the sum of true positives and false positives.

• The harmonic mean of Precision and Recall.

• The proportion of true results among the total number of cases examined.

• The ratio of true positives to all observations in the actual class.

What is Accuracy in the context of classification?

• The ratio of true positives to the sum of true positives and false positives.

• The proportion of true results among the total number of cases examined.

• The ratio of correctly predicted observations to the total observations.

• The harmonic mean of Precision and Recall.

What does Precision measure in the context of classification models?

• The ratio of correctly predicted positive observations to the total predicted positives.

• The ratio of correctly predicted positive observations to all observations in the actual class.

• The proportion of true results among the total number of cases examined.

• The harmonic mean of Precision and Recall.

What does Recall measure in the context of classification models?

• The ratio of correctly predicted positive observations to the total predicted positives.

• The ratio of correctly predicted positive observations to all observations in the actual class.

• The proportion of true results among the total number of cases examined.

• The harmonic mean of Precision and Recall.

What is the purpose of padding in Convolutional layers?

• To reduce the spatial dimensions of feature maps.

• To introduce non-linearity.

• To apply filters to input data.

• To preserve spatial dimensions after convolution.

Which parameter is not associated with Convolutional layers?

• Filter size (kernel size)

• Stride

• Padding

• Learning rate

Which layer in a CNN is responsible for reducing the spatial dimensions of feature maps?

• Convolutional layer

• Activation layer

• Pooling layer

• Fully connected layer

What is the primary task of image classification?

• Detecting objects in images

• Segmenting images into regions

• Assigning labels or categories to images based on content

• Transforming images to a different color space

What is the main advantage of using CNNs for image classification compared to traditional machine learning approaches?

• CNNs require less computational power.

• CNNs automatically extract relevant features from images.

• CNNs have a simpler architecture.

• CNNs are less prone to overfitting.