MCQs

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Overall MCQs for Reference

Which of the following algorithms is best suited for binary classification problems?
a. Linear regression
b. Decision trees
c. K-means clustering
d. PCA
Answer: b. Decision treeseasyexamnotes+1
What is the main assumption behind the Naïve Bayes classifier?
a. Linear separability
b. Feature independence
c. Data normality
d. Overfitting risk
Answer: b. Feature independencesanfoundry+1
Which metric is commonly used to evaluate the performance of a classification model?
a. Mean squared error
b. F1 score
c. Adjusted R-squared
d. Silhouette score
Answer: b. F1 scoreaionlinecourse+1
Logistic regression is most suitable for:
a. Multiclass classification
b. Dimensionality reduction
c. Binary classification
d. Clustering
Answer: c. Binary classificationsanfoundry+1
Support Vector Machines (SVMs) use what to separate classes?
a. Nearest neighbors
b. Hyperplanes
c. Tree structures
d. Means clustering
Answer: b. Hyperplanesaimcqs+1
Which algorithm is an ensemble method for classification tasks?
a. Random Forest
b. PCA
c. KNN
d. Polynomial regression
Answer: a. Random Forestaimcqs+1
Which classifier works by assigning the class most common among its nearest neighbors?
a. Decision tree
b. Logistic regression
c. K-Nearest Neighbors (KNN)
d. SVM
Answer: c. K-Nearest Neighbors (KNN)sanfoundry+1
The output variable in a classification problem is:
a. Continuous
b. Discrete
c. Ordinal only
d. Never binary
Answer: b. Discretefutureskillsacademy+1
What type of learning is used in classification models?
a. Unsupervised
b. Reinforcement
c. Supervised
d. Semi-supervised
Answer: c. Supervisedaimcqs+1
What is the goal of classification algorithms?
a. Predict continuous values
b. Assign input data to classes
c. Reduce dimensionality
d. Detect outliers
Answer: b. Assign input data to classessanfoundry+1
Which classifier can handle both categorical and numerical features effectively?
a. Naïve Bayes
b. Decision Trees
c. Linear Regression
d. Cluster Analysis
Answer: b. Decision Treesscribd+1
The Gini index is used in which model?
a. Logistic Regression
b. SVM
c. Decision Tree
d. K-means
Answer: c. Decision Treescribd+1
Which algorithm is based on Bayes’ theorem?
a. Decision tree
b. Naïve Bayes
c. Logistic regression
d. SVM
Answer: b. Naïve Bayesaimcqs+1
What is overfitting in the context of classification?
a. Low complexity
b. High accuracy on new data
c. Model fits noise in training data
d. No influence by outliers
Answer: c. Model fits noise in training dataplacementpreparation+1
The ROC curve is used to evaluate:
a. Regression accuracy
b. Data clustering
c. Classification threshold performance
d. SVM regularization
Answer: c. Classification threshold performanceaionlinecourse+1
Which algorithm is NOT suitable for multi-class classification?
a. Random Forest
b. Multinomial Logistic Regression
c. One-vs-All SVM
d. Linear Regression
Answer: d. Linear Regressionfutureskillsacademy+1
Decision Tree pruning helps to:
a. Increase tree depth
b. Prevent overfitting
c. Increase computation time
d. Reduce accuracy
Answer: b. Prevent overfittingscribd+1
Which is a non-parametric classification model?
a. Logistic Regression
b. Decision Tree
c. Linear Regression
d. Gaussian Naïve Bayes
Answer: b. Decision Treescribd+1
Which model is most likely to suffer from high bias?
a. Deep Neural Network
b. Decision Tree (small depth)
c. Random Forest
d. KNN with k=1
Answer: b. Decision Tree (small depth)sanfoundry+1
What is the main job of the Softmax function in classification?
a. Calculate error
b. Predict probabilities over classes
c. Generate clusters
d. Reduce features
Answer: b. Predict probabilities over classesinterviewbit+1
Which classifier is best for text classification?
a. Linear Regression
b. Clustering
c. Naïve Bayes
d. K-means
Answer: c. Naïve Bayesaimcqs+1
Entropy in Decision Tree algorithms measures:
a. Feature importance
b. Data purity
c. Distance between points
d. Data variance
Answer: b. Data purityscribd+1
Which model is extensively used for image recognition classification?
a. Decision Tree
b. K-means
c. Convolutional Neural Networks
d. Naïve Bayes
Answer: c. Convolutional Neural Networksigmguru+1
A linear decision boundary is typical of:
a. Naïve Bayes
b. SVM with linear kernel
c. Random Forest
d. Decision Trees
Answer: b. SVM with linear kernelsanfoundry+1
Ensemble classification models generally:
a. Work worse than single models
b. Overfit frequently
c. Combine multiple models for better results
d. Are suitable only for regression
Answer: c. Combine multiple models for better resultsfutureskillsacademy+1
Which classifier is sensitive to irrelevant features and feature scaling?
a. KNN
b. Logistic Regression
c. Naïve Bayes
d. SVM
Answer: a. KNNfutureskillsacademy+1
Which of the following is a discriminative model?
a. Naïve Bayes
b. Hidden Markov Model
c. Logistic Regression
d. Gaussian Mixture Model
Answer: c. Logistic Regressionfutureskillsacademy+1
Bagging helps improve stability in:
a. Decision Trees
b. Linear Regression
c. PCA
d. SVM
Answer: a. Decision Treesdiptisurvase.wordpress+1
Which method is suitable for dealing with class imbalance in classification?
a. Data reduction
b. SMOTE oversampling
c. Feature scaling
d. Dropout regularization
Answer: b. SMOTE oversamplingfutureskillsacademy+1
Which is true about Random Forest?
a. Uses a single decision tree
b. Prone to overfitting always
c. Utilizes majority voting across decision trees
d. Cannot handle categorical data
Answer: c. Utilizes majority voting across decision treesdiptisurvase.wordpress+1
Which model finds a maximum margin hyperplane?
a. KNN
b. Logistic Regression
c. SVM
d. Naïve Bayes
Answer: c. SVMaimcqs+1
The “One-vs-All” approach is used in:
a. Clustering
b. Multi-class classification with binary classifiers
c. Regression
d. Dimensionality reduction
Answer: b. Multi-class classification with binary classifierssanfoundry+1
L1 regularization is used in which classification technique to enforce sparsity?
a. KNN
b. Logistic Regression
c. Naïve Bayes
d. Decision Trees
Answer: b. Logistic Regressionfutureskillsacademy+1
Which classifier is commonly used for medical diagnosis?
a. Naïve Bayes
b. KNN
c. Decision Tree
d. CNN
Answer: c. Decision Treescribd+1
A confusion matrix is useful for:
a. Evaluating binary and multiclass classification
b. Regression analysis
c. Dimensionality reduction
d. Model validation only
Answer: a. Evaluating binary and multiclass classificationaionlinecourse+1
Threshold moving is a technique used to address:
a. Overfitting
b. Multicollinearity
c. Class imbalance
d. Prediction speed
Answer: c. Class imbalanceplacementpreparation+1
Which classifier’s performance is highly affected by the curse of dimensionality?
a. KNN
b. Logistic Regression
c. Naïve Bayes
d. Random Forest
Answer: a. KNNsanfoundry+1
Which of the following can both classify and regress?
a. Decision Trees
b. K-means
c. SVM
d. Naïve Bayes
Answer: a. Decision Treesaimcqs+1
What is the key feature of the AdaBoost algorithm?
a. Uses original data only
b. Focuses on misclassified samples
c. Reduces model variance
d. Penalizes correct predictions
Answer: b. Focuses on misclassified samplesigmguru+1
Random Forest handles overfitting of a single decision tree by:
a. Using fewer features
b. Combining multiple trees
c. Applying PCA first
d. Ignoring pruning
Answer: b. Combining multiple treesdiptisurvase.wordpress+1
In logistic regression, what is the predicted output bounded between?
a. 0 and 100
b. -1 and 1
c. 0 and 1
d. Unlimited range
Answer: c. 0 and 1interviewkickstart+1
What is the main output of the sigmoid function?
a. Categorical value
b. Continuous value between 0 and 1
c. Integer label
d. Cluster index
Answer: b. Continuous value between 0 and 1interviewkickstart+1
The main goal of model validation in classification is to:
a. Reduce model size
b. Assess performance on unseen data
c. Increase training speed
d. Add new features
Answer: b. Assess performance on unseen datascribd+1
Which approach is used to boost weak learners?
a. Bagging
b. Adaboost
c. PCA
d. Lasso
Answer: b. Adaboostfutureskillsacademy+1
Which data split ratio is most common in train-test evaluation of classification models?
a. 20:80
b. 80:20
c. 60:40
d. 50:50
Answer: b. 80:20placementpreparation+1
Which is not an ensemble method in classification models?
a. Bagging
b. Stacking
c. Gradient boosting
d. Linear regression
Answer: d. Linear regressiondiptisurvase.wordpress+1
Which metric is NOT used for classification task evaluation?
a. Recall
b. Precision
c. R-squared
d. Area under ROC
Answer: c. R-squaredaionlinecourse+1
Early stopping is mainly used in classification models utilizing:
a. Logistic regression
b. Random Forest
c. Gradient boosting or neural networks
d. PCA
Answer: c. Gradient boosting or neural networksigmguru+1
Which algorithm product is a probability estimate for each class?
a. Logistic Regression
b. Decision Tree
c. KNN
d. SVM
Answer: a. Logistic Regressioninterviewkickstart+1
Which is a key difference between discriminative and generative classification models?
a. Discriminative models model p(x)
b. Discriminative models model p(y|x)
c. Generative models predict feature scaling
d. Discriminative models always underfit
Answer: b. Discriminative models model p(y|x) futureskillsacademy+1

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