Top 5 Machine Learning Quiz Questions with Answers explanation, Interview
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Machine learning MCQ - Set 17
1. The partitions in a classification are _________ if the entropy is high.
(a) Pure
(b) Not pure
(c) Useless
(d) Low noise
Answer: (b) Not pure Entropy and High entropy Entropy is a measure of uncertainty. High entropy means the data has high variance and thus contains a lot of information and/or noise.
High VS Low entropy Data with full entropy is completely random and no meaningful patterns can be found. Low entropy data provides the ability or possibility to predict forthcoming generated values. |
2. A measure of goodness of fit for the estimated regression equation is the
(a) Multiple coefficient of determination
(b) Mean square due to error
(c) Mean square due to regression
(d) All of the above
Answer: (c) Mean square due to regression (MSR) Mean square due to regression or regression mean square (MSR) is obtained by dividing the regression sum of squares by its degree of freedom. The regression sum of squares (SSR) and the regression mean square (MSR) are always identical for the simple linear regression model. Estimated regression equation It is an equation constructed to model the relationship between dependent and independent variables. For simple linear regression, the least squares estimates of the model parameters β0 and β1 are denoted b0 and b1. Using these estimates, an estimated regression equation is constructed: ŷ = b0 + b1x . A primary use of the estimated regression equation is to predict the value of the dependent variable when values for the independent variables are given.
A goodness-of-fit test It refers to measuring how well do the observed data correspond to the fitted (assumed) model. [For more, please refer here] |
3. A regression model in which more than one independent variable is used to predict the dependent variable is called
(a) simple linear regression model
(b) multiple regression model
(c) independent model
(d) none of the above
Answer: (b) Multiple regression model Regressions based on more than one independent variable are called multiple regressions. Multiple linear regression is an extension of simple linear regression. Here, a dependent variable is modeled as a function of several independent variables with corresponding coefficients, along with the constant term. Multiple regression requires a minimum of two or more predictor variables, and this is why it is called multiple regression. Multiple regression will be good at explaining the relationship of the independent variables to the dependent variables if those relationships are linear. |
4. The average positive difference between computed and desired outcome values is ______ .
(a) Root mean squared error
(b) Mean squared error
(c) Mean absolute error
(d) Mean positive error
Answer: (c) Mean absolute error Absolute Error is the amount of error in your measurements. It is the difference between the measured value and “true” value. Mean absolute error is the average of all absolute errors.
Mean Absolute Error (MAE): MAE measures the average magnitude of the errors in a set of predictions, without considering their direction. It’s the average over the test sample of the absolute differences between prediction and actual observation where all individual differences have equal weight. [For more, please refer here] |
5. A survey is taken from a randomly selected sample of 100 students on whether they had ever played Cricket. 25% (0.25) of the 100 students said they had played Cricket. Which one of the following statements about the number 0.25 is correct?
(a) It is a sample proportion
(b) It is a population proportion
(c) It is a random number
(d) It is an error
Answer: (a) It is a sample proportion Sample proportion The sample proportion is the proportion of individuals in a sample sharing a certain trait, denoted ˆp. |
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