WorksheetsSeason 6 #Spaic ML 2 Weekly Quiz
Total questions: 20
Worksheet time: 12mins
What is not a RNN in Machine Learning?
one output to many inputs
many inputs to a single output
RNNs for non-sequential input
many inputs to many outputs
What is bootstrapping?
Combining a candidate and randomly selecting a set of subsamples
Splitting a candidate and randomly selecting a set of subsamples
Dividing true positives by true positives plus false negatives
Dividing false positives by true positives plus true negatives
What explains an autoassociative network in machine learning?
a neural network that contains no loops
a neural network that contains feedback
a neural network that has only one loop
none of the above
What does MSE calculate?
Classifications
Bias
Entropy
Error
What is represented by elements per dimension in TensorFlow?
Tensorshape
Variables
N-dimensional arrays
Tensors
What characterizes unlabeled examples in machine learning?
There is prior knowledge
There is plenty of confusing knowledge
There is no confusing knowledge
There is no prior knowledge
What does the K-nearest neighbor model do?
Classifies data for a base algorithm
Classifies data for regression analysis
Losing the k-nearest neighbors in training data
Classifies data to the least frequent category
What is the output of a regression model?
Malleability
Uncertainty
Probability
Reliability
Below are three scatter plots for two features (Image 1, 2 & 3 from left to right). which of the following is/are examples of multi-collinear features?
Features in Image 1
Features in Image 2
Features in Image 2 & 3
Features in Image 1 & 2
Suppose you have identified multi-collinear
features. Which of the following action(s) would you perform next?
1.Remove both collinear variables.
2.Instead of removing both variables, we can remove only one variable.
3.Removing correlated variables might lead to loss of information. In order to retain those variables, we can use penalized regression models like ridge or lasso regression
Only 1
Only 2
Either 2 or 3
Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data. Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case?
Accuracy metric is not a good idea for imbalanced class problems.
Accuracy metric is a good idea for imbalanced class problems.
Precision and recall metrics are good for imbalanced class problems.
Precision and recall metrics aren’t good for imbalanced class problems.
In ensemble learning, you aggregate the predictions for weak learners, so that an ensemble of these models will give a better prediction than prediction of individual models. Which of the following statements is / are true for weak learners used in ensemble model?
They don’t usually overfit.
They have high bias, so they cannot solve complex learning problems
They usually overfit.
You can evaluate the performance of a binary class classification problem using different metrics such as accuracy, log-loss, F-Score. Let’s say, you are using the log-loss function as evaluation metric.Which of the following option is / are true for interpretation of log-loss as an evaluation metric?
If a classifier is confident about an incorrect classification, then log-loss will penalise it heavily.
For a particular observation, the classifier assigns a very small probability for the correct class then the corresponding contribution to the log-loss will be very large.
Lower the log-loss, the better is the model.
The most widely used metrics and tools to assess a classification model are:
Confusion matrix
Cost-sensitive accuracy
Area under the ROC curve
How do you handle missing or corrupted data in a dataset?
Drop missing rows or columns
Replace missing values with mean/median/mode
Assign a unique category to missing values
When performing regression or classification, which of the following is the correct way to preprocess the data?
Normalize the data → PCA →training
PCA → normalize PCA output → training
Normalize the data → PCA → normalize PCA output → training
Which of the following is an example of feature extraction?
Constructing a bag of words vector from an email
Applying PCA projects to a large high-dimensional data
Removing stopwords in a sentence
Which of the following techniques can be used for normalization in text mining?
Stemming
Lemmatization
Stop Word Removal
The effectiveness of an SVM depends upon
selection of kernel
kernel parameters
Soft Margin Parameter C
All of these
Adding a non-important feature to a linear regression model may result in
Increase in R square
Decrease in R square
No effect
None of the above
