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Advanced Machine Learning Quiz Questions and Answers

Answer :
  • On-policy algorithms update the policy during learning, while off-policy algorithms keep the policy fixed.

Explanation :

On-policy algorithms update the policy during learning, while off-policy algorithms keep the policy fixed. This means that on-policy algorithms can learn from their own experience, while off-policy algorithms rely on experience from a different policy.
Answer :
  • Stratified sampling

Explanation :

Stratified sampling is a method for ensuring that the training and testing sets have the same proportion of classes as the original dataset. However, it is not a method for splitting the data into training and testing sets.
Answer :
  • Reduced cost

Explanation :

Distributing TensorFlow across devices and servers can improve performance, increase scalability, and improve accuracy, but it may also increase costs due to the need for additional hardware and infrastructure.
Answer :
  • Sigmoid

Explanation :

Sigmoid activation function is commonly used in the output layer of an autoencoder for image reconstruction. It maps the output values to a range between 0 and 1, which is suitable for image pixel values.
Answer :
  • A) Removing the samples with missing values
    B) Imputing the missing values with the mean or median
    C) Creating a new category for missing values
    All of the above

Explanation :

Removing the samples with missing values, imputing the missing values with the mean or median, and creating a new category for missing values are all methods to handle missing values in decision trees.
Answer :
  • Binary Cross-Entropy

Explanation :

Binary Cross-Entropy is a common loss function used in CNNs for binary classification. It measures the difference between the predicted probability distribution and the true probability distribution.
Answer :
  • Synchronous training updates all devices at the same time, while asynchronous training updates devices independently.

Explanation :

In synchronous training, all devices are updated at the same time, while in asynchronous training, devices are updated independently, which can result in slower convergence but may be faster overall.
Answer :
  • a={5,6,7}

Explanation :

There exists add method for set data type. However 5 isn't added again as set consists of only non-duplicate elements and 5 already exists in the set. Execute in python shell to verify.
Answer :
  • They are prone to overfitting

Explanation :

Decision trees are prone to overfitting in regression problems, which means they can become too complex and fit the training data too closely, resulting in poor performance on new data.