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WorksheetsMachine Learning
Total questions: 20
Worksheet time: 10mins
What type of Machine Learning Algorithm is suitable for predicting the continuous dependent variable?
Logistic Regression
Linear Regression
Decision Tree Classifier
KNN Classifier
What type of Machine Learning Algorithm is suitable for predicting the dependent variable with two different values?
Logistic Regression
Linear Regression
Multiple Linear Regression
Polynomial Regression
The correlation in between mobile usage and exam score of a person found to be -2.2. What is your inference from the above statement.
Mobile usage is positively correlated with exam score
Mobile usage is negatively correlated with exam score
None of the mentioned
Need some other information
The residual is the difference in between ________________
actual value of y and the estimated value of y
actual value of x and the estimated value of x
actual value of y and the estimated value of x
actual value of x and the estimated value of y
Suitable evaluation metric for measuring the performance of a given regression model is
Mean Absolute Error
Root Mean Square Error
Precision
Recall
If we decrease the input variable by one unit in a simple linear regression model. How many units of the output variable will change?
reduced by Intercept
increased by Intercept
increased by Slope
reduced by Slope
Appropriate chart for visualizing the linear relationship between two variables is _________________
Scatter plot
Barchart
Histograms
None of Mentioned
The Number of coefficients required to estimate a simple linear regression?
1
2
0
3
KNN Algorithm can be used for
Only for Classification
Only for Regression
Both Classification and Regression
None of the Mentioned
KNN is ___________ algorithm
Non-parametric and Lazy Learning
Parametric and Lazy Learning
Parametric and Eager Learning
Non-parametric and Eager Learning
What kind of distance metric(s) are suitable for categorical variables to finding the closest neighbors
Euclidean Distance
Manhattan distance
Minkowski distance
Hamming distance
What kind of distance metric(s) are suitable for continuous variables to find the closest neighbors
Euclidean Distance
Manhattan distance
Minkowski distance
Hamming distance
KNN algorithm appropriate for
Lower number of features
Large number of features
No such restriction on number of features
None of the Mentioned
KNN algorithm requires
More time for training
More time for testing
Equal time for training and testing
None of the Mentioned
The entropy of a given dataset is zero. This statement implies what?
further splitting is required
no further splitting is required
Need some other information to decide splitting
None of the Mentioned
If the given dataset contains 100 observations out of 50 belongs to class1 and other 50 belongs to class2. What will be the entropy of the given dataset?
0
1
-1
0.5
How do you choose the root node while constructing a Decision Tree?
An attribute having high entropy
An attribute having largest information gain
An attribute having high entropy and Information gain
None of the Mentioned
Chose the correct criterion for Decision Tree Classifier in sklearn package
Gini
Entropy
Information Gain
Random
In a Decision Tree Leaf Node represents_____________
One of the Class Label
One of the complete observation
One of the attribute
None of the Mentioned
Consider the above Confusion Matrix of a classifier and choose the correct statements
Accuracy is 84%
Misclassification Rate is 16%
Type-I Error is 6
Type-II Error is 10
