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WorksheetsSeason 3 #Spaic Machine learning Weekly Quiz
Total questions: 20
Worksheet time: 15mins
The key challenge (s) to successfully training a machine learning Model :
Selecting features
Choosing the right algorithm
Tuning the hyper-parameter
Evaluating the metric
All of the above
Automated ML test multiple algorithm and hyper-parameter in sequence and return the best one :
True
False
The result Model Of Automated ML can be customized :
True
False
What are the user Input to Automated ML process ?
Dataset
Target metric
Constraint (cost / time)
Algorithm
To create an Automated ML run :
We need to select the algorithm first
We need to select the dataset first
We need to select the compute target first
There is no need to define the target column in Automated ML run
True
False
What are the additional configuration for the regression task in Automated ML :
Primary metric
Blocked algorithms
Target columns
Training job time (hours)
Metric score threshold
You had this result after the execution of Automated ML with a regression task (What that's it mean)
Max Abs Scaler . DecisionTree is the best model
Max Abs Scaler .RandomForest is the best model
The metric used is Normalized root mean square
The metric value of the best model is 0.0533
What is the resulted evaluation chart after the execution of Automated ML with a regression task
ROC curve
Confusion matrix
Predict vs True
Residual Histogram
Which of the following is a widely used and effective ML algorithm based on the idea of bagging?
Decision Tree
Regression
Classification
Random Forest
The most widely used metric(s) and tool(s) to assess a classification model is/are :
Confusion Matrix
Cost-sensitive accuracy
Area under the ROC curve
All of the above
Which of the following is a good dataset characteristic?
Large enough to yield meaningful results
Is representative of the dataset as a whole
Large enough to yield meaningful results and Is representative of the dataset as a whole
None of the above
Which of the following is a disadvantage of decision trees?
Factor analysis
Decision trees are robust to outliers
Decision trees are prone to be overfit
None of the above
How do you handle missing or corrupted data in a data set?
Drop missing rows or columns
Replace missing values with mean/median/mode
Assign a unique category to missing values
All of the above
What is the purpose of performing cross-validation?
To assess the predictive performance of models
To judge how the trained model performs outside the sample on test data
All of the above
When performing regression or classification,which of the following is a correct way to preprocess the data?
Normalize the data -> PCA ->training
PCA -> Normalize PCA output -> training
Normalize the data -> PCA -> Normalize PCA output -> training
None of the above
Which of the following is an example of feature extraction?
Constructing bag of words vector from an email
Applying PCA projects to a large high-dimensional data
Removing stopwords in a sentence
All of the above
Which of the following is true about Naive Bayes?
Assumes that all the features in a dataset are equally important
Assumes that all the features in a dataset are independent
None of the above options
Which of the following statements about regularization is not correct?
Using too large a value of lambda can cause your hypothesis to underfit the data
Using too large a value of lambda can cause your hypothesis to overfit the data
Using a very large value of lambda cannot hurt the performance of your hypothesis
None of the above
How can you prevent a clustering algorithm from getting stuck in bad local optima?
Set the same seed value for each run
Use multiple random initializations
None of the above
