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WorksheetsML_Quiz-2
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
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 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 continuous variables to find the closest neighbors
Euclidean Distance
Manhattan distance
Minkowski distance
Hamming distance
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
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
State True or False
For k cross-validation, larger k value implies more bias.
True
False
Cross validation is used for
Comparing predictors
Selecting parameters in prediction function
Selecting variables to include in a model
All of the mentioned
Suppose you are dealing with 4 class classification problem and you want to train a SVM model on the data for that you are using One-vs-all method. Now, say for training 1 time in one vs all setting the SVM is taking 10 second. How many seconds would it require to train one-vs-all method end to end?
20
40
80
60
Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression.You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.
Which of the following is true for X1?
Relation between the X1 and Y is weak
Relation between the X1 and Y is strong
Relation between the X1 and Y is nutral
Correlation can’t judge the relationship
Regarding Bias and Variance ,which of the following statement is True?
Model which overfit has high bias and high variance
Model which overfits have Low bias and low variance
Model which overfits has high Bias and Low variance
Model which overfits has low Bias and High Variance
Which of the following is true about Lasso and Ridge Regression?
Ridge regression uses subset selection of features
Lasso regression uses subset selection of features
Both uses subset selection of features
None of them are used for subset selection of features
