MCQs

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Overall MCQs for Reference

What does the average probability usually represent in the context of machine learning?
a. Likelihood across data points
b. Maximum observed value
c. Median of predictions
d. Standard deviation
Answer: a. Likelihood across data pointsgeeksforgeeks
What is the mathematical formula for calculating probability of an event A?
a. P(A) = Number of unfavorable outcomes / Total outcomes
b. P(A) = Number of favorable outcomes / Total outcomes
c. P(A) = Number of successes / Number of trials
d. P(A) = Sample mean / Population mean
Answer: b. P(A) = Number of favorable outcomes / Total outcomesgeeksforgeeks
Which machine learning algorithm uses probability to classify data points?
a. Decision Tree
b. Naive Bayes
c. KMeans
d. Linear Regression
Answer: b. Naive Bayesgeeksforgeeks
In binary classification, what is the range of predicted probabilities?
a. 0 to 2
b. -1 to +1
c. 0 to 1
d. Any integer
Answer: c. 0 to 1geeksforgeeks
What does “expected value” mean in probability theory for machine learning?
a. Most common value
b. Median value
c. Mean value
d. Maximum value
Answer: c. Mean valuesanfoundry
What is the equation for the probability of the intersection of A and B (when independent)?
a. P(A+B)
b. P(A) * P(B)
c. P(A|B)
d. P(A) / P(B)
Answer: b. P(A) * P(B)d2l
When averaging probabilities across multiple models, what ensemble technique is commonly used?
a. Bagging
b. Boosting
c. Feature selection
d. PCA
Answer: a. Bagginggyansetu
What is the primary use of the sigmoid function in logistic regression?
a. Calculate loss
b. Regularize weights
c. Calculate probability
d. Transform features
Answer: c. Calculate probabilitydevelopers.google
What does a probability of 0 mean for a predicted class?
a. Impossible event
b. Certain event
c. Moderate likelihood
d. Random event
Answer: a. Impossible eventgeeksforgeeks
What distribution deals with the average number of successes in n Bernoulli trials?
a. Gaussian
b. Binomial
c. Categorical
d. Uniform
Answer: b. Binomialgeeksforgeeks
Which probability distribution is used for binary outcomes in machine learning?
a. Poisson
b. Bernoulli
c. Categorical
d. Exponential
Answer: b. Bernoulligeeksforgeeks
In Bayes theorem, what does the denominator represent?
a. Prior probability
b. Evidence
c. Posterior
d. Loss function
Answer: b. Evidenced2l
Which value measures the average spread in a probability distribution?
a. Mode
b. Variance
c. Median
d. Mean
Answer: b. Variancesanfoundry
Which of the following statements about cumulative distribution functions (CDF) is correct?
a. CDF is non-decreasing and right-continuous
b. CDF is always decreasing
c. CDF is left-continuous
d. CDF is constant
Answer: a. CDF is non-decreasing and right-continuoussanfoundry
What is the square root of the variance called?
a. Empirical mean
b. Standard deviation
c. Mode
d. Skewness
Answer: b. Standard deviationsanfoundry
For which algorithm is averaging conditional probabilities essential?
a. Random forest
b. Naive Bayes
c. SVM
d. Gradient boosting
Answer: b. Naive Bayesgeeksforgeeks
What is the result of averaging probabilities output by multiple classifiers?
a. Bagged prediction
b. Boosted prediction
c. Decision boundary
d. Overfitting
Answer: a. Bagged predictiongyansetu
Which model uses probability density functions to characterize continuous variables?
a. Bernoulli
b. Linear regression
c. Gaussian mixture
d. Decision tree
Answer: c. Gaussian mixturegeeksforgeeks
Which term describes a predicted probability that is exactly 0.5 for an event?
a. Impossible
b. Equally likely
c. Certain
d. Outlier
Answer: b. Equally likelygeeksforgeeks
What does the area under the probability density function sum to?
a. 0
b. 1
c. Infinity
d. Posterior
Answer: b. 1geeksforgeeks
In ML, which probability describes “uncertainty of predictions”?
a. Posterior probability
b. Prior probability
c. Empirical probability
d. Average class probability
Answer: a. Posterior probabilitygeeksforgeeks
What is the probability assigned by the Naive Bayes for unseen feature values?
a. Zero
b. Average
c. One
d. Smoothing
Answer: d. Smoothinggeeksforgeeks
What characterizes the output of a probabilistic classifier?
a. Class label only
b. Probability and class label
c. Outlier score
d. Feature importance
Answer: b. Probability and class labelgeeksforgeeks
What is typically maximized in Maximum Likelihood Estimation?
a. Data variance
b. Joint probability
c. Posterior probability
d. Feature relevance
Answer: b. Joint probabilitytowardsdatascience
What is the formula for the expected value in a binomial distribution?
a. np
b. n/p
c. p/n
d. n + p
Answer: a. nptestbook
What is the probability of success in a Bernoulli trial if the event never occurs?
a. 1
b. 0
c. np
d. p^2
Answer: b. 0geeksforgeeks
For regression tasks, how is average probability used?
a. To estimate mean squared error
b. To measure likelihood of outcomes
c. To balance the classes
d. To calculate Gini
Answer: b. To measure likelihood of outcomestowardsdatascience
Which probabilistic method is commonly used in deep learning for uncertainty?
a. Feature scaling
b. Variational inference
c. Min-max normalization
d. Principal Component Analysis
Answer: b. Variational inferencegeeksforgeeks
In cross-validation, how is average probability typically interpreted?
a. Mean probability prediction across folds
b. Best single prediction
c. Training accuracy
d. Highest variance
Answer: a. Mean probability prediction across foldsgeeksforgeeks
Why is probability theory essential in machine learning?
a. For deterministic solutions
b. For handling uncertainty
c. For increasing bias
d. For data cleaning
Answer: b. For handling uncertaintygeeksforgeeks
What is an “average probability” over multiple independent events?
a. Their sum
b. Their mean
c. Their product
d. Their difference
Answer: b. Their meangeeksforgeeks
Which metric uses probability for binary classification performance?
a. Accuracy
b. Log-loss
c. RMSE
d. F1 score
Answer: b. Log-losstowardsdatascience
For a uniform distribution, what is the average probability for any outcome?
a. 1/N
b. 0.5
c. 1
d. N
Answer: a. 1/Ngeeksforgeeks
Which concept uses average probability in outlier detection?
a. Multivariate normal
b. Z-score
c. Local Outlier Factor
d. PCA
Answer: c. Local Outlier Factortowardsdatascience
The sum of probabilities across all mutually exclusive events equals:
a. Zero
b. Infinity
c. One
d. Mean
Answer: c. Onegeeksforgeeks
For probability calibration, what is adjusted?
a. Predicted class labels
b. Average predicted probabilities
c. Feature values
d. Loss function
Answer: b. Average predicted probabilitiestowardsdatascience
What is the result called when naive averaging probability leads to overconfident predictions?
a. Calibration error
b. Underfitting
c. Statistical bias
d. Noise
Answer: a. Calibration errortowardsdatascience
What technique can be used to correct average probability distribution in classification?
a. Platt scaling
b. Feature selection
c. Bagging
d. Min-max normalization
Answer: a. Platt scalingtowardsdatascience
What type of probability is estimated in Bayesian networks for each node?
a. Marginal probability
b. Joint probability
c. Average probability
d. Posterior probability
Answer: d. Posterior probabilitygeeksforgeeks
Why is averaging probabilities important in ensemble models?
a. Reduces bias
b. Improves stability and generalization
c. Increases overfitting
d. Decreases variance
Answer: b. Improves stability and generalizationgyansetu
What does the “mean squared error” metric assume about probability distributions?
a. Gaussian distribution
b. Uniform distribution
c. Bernoulli distribution
d. Binomial distribution
Answer: a. Gaussian distributiontowardsdatascience
If the probability of an event is p, what is the probability of it not occurring?
a. p
b. 1-p
c. 1
d. p^2
Answer: b. 1-pgeeksforgeeks
What happens to the average probability estimate when the sample size increases?
a. More variance
b. Higher bias
c. More accuracy
d. Less sensitivity
Answer: c. More accuracygeeksforgeeks
What is the effect of averaging probabilities from non-independent models?
a. Inflated confidence
b. Reduced interpretability
c. Bias toward correlated models
d. Unbiased estimate
Answer: c. Bias toward correlated modelstowardsdatascience
What is meant by maximum average probability in binary classification?
a. Most likely event
b. Least likely event
c. Average of all predicted probabilities at maximum threshold
d. Mean of class labels
Answer: a. Most likely eventtowardsdatascience
In a binomial setting, if p=0.5 and n=10, what is the expected average success?
a. 5
b. 0.5
c. 10
d. 50
Answer: a. 5testbook
When are predicted probabilities averaged in cross-validated ensemble models?
a. After each fold
b. Before training
c. At prediction time
d. After feature selection
Answer: c. At prediction timegeeksforgeeks
What does a probability value of 1 indicate in machine learning predictions?
a. Impossible
b. Certain/Guaranteed
c. Moderate
d. Conditional
Answer: b. Certain/Guaranteedgeeksforgeeks
Which technique averages probability outputs for calibration and interpretability?
a. Isotonic regression
b. Ridge regression
c. PCA
d. SVM
Answer: a. Isotonic regressiontowardsdatascience
What is the average probability in a dataset with balanced binary labels?
a. 0.25
b. 0.5
c. 1
d. Depends on class skew
Answer: b. 0.5geeksforgeeks

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