WorksheetsUNIT II Supervised Learning QUIZ Part 1
Total questions: 55
Worksheet time: 58mins
Suppose you are working on weather prediction, and use a learning algorithm to predict tomorrow's temperature (in degrees Centigrade/Fahrenheit). Would you treat this as a classification or a regression problem?
Regression
Classification
None of the Above
What is the main goal of machine learning?
To make computers intelligent
To automate manual tasks
To enable computers to learn from data
To create self-aware machines
What is the purpose of regularization in linear regression?
To make the model more complex
To avoid underfitting
To encourage overfitting
To reduce the complexity of the model
What is the primary goal of feature engineering in supervised machine learning?
To reduce model complexity
To improve model interpretability
To enhance model performance
To increase the number of training samples
For the Given Dataset the Covariance and correlation values are
136, 0.996
137, 0.771
152, 0.521
142, 0.996
For the given dataset the values of regression parameters are
15.509, 4.162
13.209, 3.502, 7.239
17.532, 15.102, 16.772
19.762, 15.302
The t test value of a slope parameter of hypothesis H: B = 0 is
1.24
30.71
14.57
11.52
The t test value of slope parameter for the Hypothesis H:B = 12 is
17.57
15.19
11.57
6.94
What are the SSE, SSR and SST Values of the given data set respectively
7.156, 5.147, 12.313
10.156, 9.147, 19.313
348.848, 27419.500 ,27768.348
350.848, 27450.500, 27801.348
What is the R-square value of the given dataset
0.897
0.787
0.987
0.657
What are the types of Supervised Learning?
Classification
Clustering
Regression
Data Validation
None of the above
Match the following
1.Univariate
2.Multivariate
3.Simple
4.Multiple
5.Linear
6.Logistic
A.Only one quantitative response variable
B.Only one predictor variable
C.The response variable is qualitative
D.All parameters enter the equation linearly, possibly after transformation of the data
E.Two or more quantitative response variables
F.Two or more predictor variables
1-A, 2-B, 3-C, 4-D, 5-E, 6-F
1-A, 2-E, 3-B, 4-F, 5-D, 6-C
1-B, 2-A, 3-C, 4-F, 5-E, 6-D
1-B, 2-D, 3-E, 4-C, 5-F, 6-A
SSE(RM) = 1254.65 and SSE(FM) = 1149.00 with degree of freedom 4 and 23 respectively. Hence the F-Test has a value of
0.528
0.627
0.567
0.573
What is the fundamental principle behind Bayesian algorithms?
Minimizing error
Maximizing accuracy
Incorporating prior knowledge
Optimizing computational complexity
Which probability distribution is often used for modeling continuous variables in Bayesian algorithms?
Bernoulli distribution
Gaussian distribution
Poisson distribution
Exponential distribution
In Bayesian Linear Regression, what is the role of the prior distribution?
It represents the uncertainty in the dependent variable
It represents the uncertainty in the independent variables
It represents the prior knowledge about the relationship between variables
It represents the noise in the data
Which algorithm is used for updating beliefs in Bayesian inference?
Forward-Backward Algorithm
Markov Chain Monte Carlo (MCMC)
Expectation-Maximization (EM)
Bayes' Theorem
In Bayesian inference, what does the posterior distribution represent?
The prior distribution
The likelihood function
The joint distribution
The updated belief after incorporating new evidence
In Bayesian decision theory, what is the role of the loss function?
To measure the accuracy of predictions
To measure the uncertainty in the data
To measure the cost of different decisions
To measure the complexity of the model
Which algorithm is used for estimating the parameters of a Bayesian non-parametric model?
Variational Inference
Markov Chain Monte Carlo (MCMC)
Expectation-Maximization (EM)
Gaussian Processes
Gradient Descent is an optimization algorithm used for,
Certain Changes in algorithm
minimizing the cost function in various machine learning algorithms
maximizing the cost function in various machine learning algorithms
remaining same the cost function in various machine learning algorithms
_____processes all the training examples for each iteration of gradient descent.
Stochastic Gradient Descent
Batch Gradient Descent
Mini Batch gradient descent
None of the above
There are how many types of Gradient Descent?
4
3
2
1
_____is a type of gradient descent which processes 1 training example per iteration.
Batch Gradient Descent
Stochastic Gradient Descent
Mini Batch gradient descent
none of these
If the cost function is convex, then it converges to a _____
global maximum
global minimum
local minimum
local maximum
Which is the fastest gradient descent?
Batch Gradient Descent
Stochastic Gradient Descent
Mini Batch gradient descent
none of these
____________ controls the magnitude of a step taken during Gradient Descent.
Parameter
Step Rate
Momentum
Learning Rate
What is the advantage of using an iterative algorithm like gradient descent ?
For Nonlinear regression problems, there is no close form solutions
Linear regression problems have multiple solutions
Linear regression problem there is no closed form solution
For ____________, the error is calculated by finding the sum of squared distance between actual and predicted values.
Regression
Classification
Clustering
None of the Above
In a multiple regression model, the following statistics are given: SSE = 100, R-square = 0.995, k = 5, and n = 15. Then, the multiple coefficient of determination adjusted for degrees of freedom is
0.955
0.930
0.900
0.855
In a multiple regression analysis, if the model provides a poor fit, this indicates that:
the sum of squares for error will be large
the standard error of estimate will be large
the multiple coefficient of determination will be close to zero
All of the above
In a multiple regression model, the mean of the probability distribution of the error variable is assumed to be:
1.0
0.0
Any value greater than 1
k, where k is the number of independent variables included in the model
To test the validity of a multiple regression model, we test the null hypothesis that the regression coefficients are all zero by applying the:
t-test
z-test
F-test
All of the Above
When the independent variables are correlated with one another in a multiple regression analysis, this condition is called:
heteroscedasticity
homoscedasticity
multicollinearity
elasticity
In a multiple regression analysis involving 6 independent variables, the sum of squares are calculated as: Total variation in Y = SSY = 900, SSR = 600 and SSE = 300. Then, the value of the F-test statistic for this model is:
150
100
50
None of the above
In multiple regression models, the values of the error variable are assumed to be:
autocorrelated
dependent of each other
independent of each other
always positive
In a multiple regression analysis involving k independent variables and n data points, the number of degrees of freedom associated with the sum of squares for error is:
k-1
n-k
n-1
n-k-1
In a multiple regression model, the probability distribution of the error variable is assumed to be:
normal
non-normal
positively skewed
negatively skewed
If the regression equation is equal to y=23.6−54.2x, then 23.6 is the _____ while -54.2 is the ____ of the regression line.
Slope, intercept
Slope, regression coefficient
Intercept, slope
Radius, intercept
The correct relationship between SST, SSR, and SSE is given by
SSR = SST + SSE
SST = SSR + SSE
SSE = SSR – SST
all of the above
A residual is defined as
The difference between the actual Y values and the mean of Y.
The difference between the actual Y values and the predicted Y values.
The predicted value of Y for the average X value.
The square root of the slope.
Which one is the least square method formula
min ∑(yi - ŷi) 2
min ∑(ŷi -yi)
min ∑(yi - ŷi) 2
min ∑(yi - ŷi)
If the slope of the regression equation y = bo + b1x is positive, then
as x increases y decreases
as x increases so does y
Either a or b is correct
as x decreases y increases
Least square method calculates the best-fitting line for the observed data by minimizing the sum of the squares of the _______ deviations.
Vertical
Horizontal
Both of these
None of these
In regression analysis, the variable that is being predicted is
the independent variable
the dependent variable
usually denoted by x
usually denoted by r
A regression analysis is inappropriate when
you have two variables that are measured on an interval or ratio scale
you want to make predictions for one variable based on information about another variable
the pattern of data points forms a reasonably straight line.
there is heteroscedasticity in the scatter plot.
A multiple regression model has the form . As increases by one unit, with and held constant, the y on average is expected to:
increase by 1 unit
increase by 12 units
decrease by 4 units
decrease by 16 units
If multicollinearity exists among the independent variables included in a multiple regression model, then:
regression coefficients will be difficult to interpret
standard errors of the regression coefficients for the correlated independent variables will increase
multiple coefficient of determination will assume a value close to zero
both (a) and (b) are correct statements
The coefficient of multiple determination ranges from:
1.0 to Infinity
0.0 to 1.0
1.0 to k, where k is the number of independent variables in the model
1.0 to n, where n is the number of observations in the dependent variable
In a simple linear regression problem, the following pairs of () are given: (6.75, 7.42), (8.96, 8.06), (10.30, 11.65), and (13.24, 12.15). Then, the sum of squares for error is
39.2500
-0.0300
4.2695
39.2800
In a multiple regression model, the value of the coefficient of multiple determination has to fall between
– 1 and + 1
0 and + 1
– 1 and 0
Any pair of real numbers
In a multiple regression model, which of the following is correct regarding the value of the value of adjusted for the degrees of freedom?
It can be negative
It has to be positive
It has to be larger than the coefficient of multiple determination
It can be larger than 1
From the given data set is it true that Cor(Y, X) = Cor(Y, Y') = 0
Yes
No
what is the t-test values for the provided coefficients 20009.5, 0.935253, 0.224337 and s.e 0.8244, 0.0500, 0.4681 respectively.
24012, 25, 0.54
24271, 18.7, 0.479
17542, 24.5, 0.724
14524, 89, 25.05
what is the t-test values for the provided coefficients -16744.4, 0.850979, 0.836991 and s.e 896.4, 0.4349, 0.0448 respectively.
20.07, 14.05, 7.47
15.23, 9.29, 5.25
-18.7, 1.96, 18.7
17.7, 2.47, 19.3
