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Practice these MCQs to strengthen your Machine Learning skills and prepare confidently for job interviews or certification exams.
Whether you’re aiming to crack data science interviews or mastering Machine Learning concepts as part of your career path — you’re in the right place.
80+ Machine Learning MCQs covering everything from supervised learning, model evaluation, algorithms, and neural networks — complete with answers to help you test and reinforce your understanding, no guesswork involved.
Section 1: Basics of Machine Learning
- Which of the following is a type of Machine Learning?
A. Supervised
B. Unsupervised
C. Reinforcement
D. All of the above
Answer: D - Which of the following is not a Machine Learning algorithm?
A. Linear Regression
B. K-Means
C. Naive Bayes
D. Bubble Sort
Answer: D - Machine Learning is a subset of:
A. Deep Learning
B. Artificial Intelligence
C. Neural Networks
D. Robotics
Answer: B - Which of the following uses labeled data?
A. Unsupervised Learning
B. Supervised Learning
C. Reinforcement Learning
D. None
Answer: B - Which is an example of classification problem?
A. Predicting house prices
B. Diagnosing a disease as positive/negative
C. Finding groups in data
D. Reducing dimensions
Answer: B - Which term refers to the difference between the predicted and actual values?
A. Loss
B. Error
C. Cost
D. All of the above
Answer: D - Which of the following is used for dimensionality reduction?
A. PCA
B. KNN
C. SVM
D. Naive Bayes
Answer: A - The curse of dimensionality is related to:
A. Overfitting
B. Too many features
C. Small datasets
D. Both A and B
Answer: D - Overfitting occurs when:
A. Model performs well on test data
B. Model learns noise in training data
C. Model generalizes well
D. None of the above
Answer: B - Which of these is a linear model?
A. Decision Tree
B. SVM with RBF Kernel
C. Linear Regression
D. K-Means
Answer: C
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Section 2: Supervised Learning
- Which of the following is a regression algorithm?
A. Logistic Regression
B. Linear Regression
C. Naive Bayes
D. KNN
Answer: B - Which algorithm can be used for both classification and regression?
A. KNN
B. Linear Regression
C. Naive Bayes
D. None
Answer: A - What is the output of a classification algorithm?
A. Continuous value
B. Discrete label
C. Cluster
D. Probability only
Answer: B - Which of the following is not used for classification?
A. SVM
B. Decision Tree
C. KNN
D. Linear Regression
Answer: D - What does the sigmoid function output range between?
A. -1 to 1
B. 0 to 1
C. -∞ to ∞
D. 1 to ∞
Answer: B - Which of these is sensitive to outliers?
A. K-Means
B. Decision Tree
C. Linear Regression
D. All of the above
Answer: C - In Logistic Regression, the output is:
A. Probability
B. Label
C. Class name
D. Integer
Answer: A - A confusion matrix is used in:
A. Clustering
B. Regression
C. Classification
D. All of the above
Answer: C - Which is not a classification metric?
A. Accuracy
B. Precision
C. R-Squared
D. Recall
Answer: C - Which ensemble method averages predictions?
A. Random Forest
B. Bagging
C. Boosting
D. Stacking
Answer: B
Section 3: Unsupervised Learning
- Which of these is an unsupervised learning task?
A. Classification
B. Regression
C. Clustering
D. Linear Regression
Answer: C - K-Means algorithm is sensitive to:
A. Initial centroids
B. Data scaling
C. Outliers
D. All of the above
Answer: D - Which technique is commonly used for market segmentation?
A. PCA
B. Clustering
C. Regression
D. Classification
Answer: B - What is the value of K in K-Means?
A. Maximum iterations
B. Number of clusters
C. Number of features
D. None
Answer: B - Which is a clustering algorithm?
A. Naive Bayes
B. DBSCAN
C. Linear Regression
D. Logistic Regression
Answer: B
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Section 4: Model Evaluation
- What does ROC curve represent?
A. Recall vs. Precision
B. Accuracy vs. Time
C. TPR vs. FPR
D. Loss vs. Epoch
Answer: C - Which score is best for imbalanced datasets?
A. Accuracy
B. F1-Score
C. Precision
D. Specificity
Answer: B - Which evaluation metric is used for regression tasks?
A. Precision
B. MSE
C. Recall
D. AUC
Answer: B - R-Squared value indicates:
A. Feature importance
B. Variance explained
C. Model complexity
D. Dataset size
Answer: B - Which is not a loss function?
A. Cross-Entropy
B. Mean Squared Error
C. Gini Index
D. Hinge Loss
Answer: C
Section 5: Neural Networks & Deep Learning
- Neural networks are inspired by:
A. Heart
B. Lungs
C. Brain
D. Bones
Answer: C - Which activation function is used in hidden layers?
A. Sigmoid
B. ReLU
C. Softmax
D. All of the above
Answer: D - Which optimizer is widely used in deep learning?
A. Gradient Descent
B. AdaGrad
C. Adam
D. Newton-Raphson
Answer: C - What is dropout used for?
A. Adding layers
B. Regularization
C. Removing bias
D. Decreasing learning rate
Answer: B - Convolutional Neural Networks are used for:
A. Text data
B. Image data
C. Tabular data
D. Audio only
Answer: B
Section 6: Advanced Concepts
- What is Transfer Learning?
A. Transferring models
B. Using pre-trained models
C. Transferring data
D. Sharing parameters
Answer: B - Which technique is used in NLP?
A. CNN
B. LSTM
C. GAN
D. DBSCAN
Answer: B - Reinforcement Learning involves:
A. Rewards and penalties
B. Labeled data
C. Unlabeled data
D. Clusters
Answer: A - Which is not a feature selection technique?
A. Chi-Square
B. PCA
C. RFE
D. Dropout
Answer: D - Which of the following is used in anomaly detection?
A. KNN
B. Isolation Forest
C. Random Forest
D. Logistic Regression
Answer: B
Section 7: Feature Engineering & Preprocessing
- What is the purpose of feature scaling?
A. Reduce number of features
B. Increase training data
C. Standardize feature range
D. Decrease model accuracy
Answer: C - Which of these is a common scaling technique?
A. One-hot encoding
B. Label encoding
C. Min-Max Scaling
D. Binning
Answer: C - Which technique is used for handling missing data?
A. Data augmentation
B. Imputation
C. Feature reduction
D. Scaling
Answer: B - One-hot encoding is used for:
A. Numerical features
B. Text features
C. Categorical features
D. Continuous labels
Answer: C - Which of the following can cause data leakage?
A. Train-test split
B. Using future data during training
C. Feature selection
D. Normalization
Answer: B - What does label encoding do?
A. Normalizes numeric values
B. Converts categories to integers
C. Drops irrelevant features
D. Fills missing values
Answer: B - Which of these techniques reduces overfitting?
A. Adding features
B. Increasing epochs
C. Regularization
D. Using noisy data
Answer: C - Which regularization technique uses L1 norm?
A. Ridge
B. Lasso
C. Elastic Net
D. Batch Norm
Answer: B - Which term describes input variables in a dataset?
A. Labels
B. Targets
C. Features
D. Errors
Answer: C - Which of the following splits data into training and test sets?
A. KMeans
B. train_test_split
C. OneHotEncoder
D. StandardScaler
Answer: B
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Section 8: Algorithms & Techniques
- Which algorithm is best suited for non-linear decision boundaries?
A. Logistic Regression
B. KNN
C. SVM with RBF Kernel
D. Linear Regression
Answer: C - What does ‘K’ in KNN represent?
A. Number of classes
B. Number of features
C. Number of neighbors
D. Kernel used
Answer: C - Naive Bayes assumes:
A. Feature independence
B. Linear relationships
C. Non-linear decision boundaries
D. Clustering nature
Answer: A - Decision Trees split data based on:
A. Mean
B. Variance
C. Gini or Entropy
D. Mode
Answer: C - Which algorithm is prone to overfitting?
A. Linear Regression
B. Decision Tree
C. Ridge Regression
D. Naive Bayes
Answer: B - What is an ensemble model?
A. Single model
B. Model trained on time series
C. Combination of multiple models
D. Reinforcement model
Answer: C - Which of these is a boosting algorithm?
A. Bagging
B. Random Forest
C. AdaBoost
D. KMeans
Answer: C - Gradient Boosting works by:
A. Reducing bias iteratively
B. Voting
C. Clustering data
D. Penalizing error
Answer: A - In Random Forest, each tree is trained on:
A. Same data
B. Different subset
C. Random noise
D. Only features
Answer: B - Which algorithm is good for high-dimensional data?
A. KNN
B. Decision Tree
C. SVM
D. Linear Regression
Answer: C
Section 9: Real-World Applications & Concepts
- Which ML task is used in spam filtering?
A. Clustering
B. Classification
C. Regression
D. Reinforcement
Answer: B - Recommendation systems use:
A. Regression
B. Classification
C. Collaborative filtering
D. Reinforcement learning only
Answer: C - What is the primary goal of unsupervised learning?
A. Predict output
B. Classify labels
C. Find structure in data
D. Calculate loss
Answer: C - Which of the following best describes underfitting?
A. Model fits noise
B. Model is too complex
C. Model misses patterns
D. Model generalizes well
Answer: C - A model performs well on training data but poorly on test data. It is:
A. Underfitted
B. Overfitted
C. Regularized
D. Accurate
Answer: B - AUC stands for:
A. Area Under Curve
B. Average Under Class
C. Accuracy Under Curve
D. All Used Classifiers
Answer: A - Which ML type is used in robotics for learning from feedback?
A. Supervised
B. Unsupervised
C. Reinforcement
D. Semi-Supervised
Answer: C - What is the full form of SVM?
A. Support Vector Machine
B. Sample Vector Model
C. Supervised Variance Model
D. Statistical Vector Map
Answer: A - Which of the following is used to avoid overfitting in neural networks?
A. Increasing layers
B. Batch normalization
C. Dropout
D. Both B and C
Answer: D - What is the role of learning rate in training?
A. Measures accuracy
B. Sets data split
C. Controls weight updates
D. Defines model size
Answer: C
Section 10: Miscellaneous & Tools
- Which Python library is commonly used for ML?
A. NumPy
B. Pandas
C. Scikit-learn
D. Flask
Answer: C - Which function is used to train a model in scikit-learn?
A. model.run()
B. model.train()
C. model.fit()
D. model.predict()
Answer: C - Which language is most used in Machine Learning?
A. Java
B. Python
C. C++
D. HTML
Answer: B - TensorFlow is developed by:
A. Facebook
B. Microsoft
C. OpenAI
D. Google
Answer: D - Which of the following is not a framework?
A. PyTorch
B. TensorFlow
C. NumPy
D. Keras
Answer: C - The train-test split ratio commonly used is:
A. 50:50
B. 60:40
C. 80:20
D. 95:5
Answer: C - Which file format is commonly used for datasets?
A. .doc
B. .csv
C. .exe
D. .pdf
Answer: B - Which is used for text classification?
A. CNN
B. LSTM
C. RNN
D. All of the above
Answer: D - Which method detects overfitting during training?
A. Validation loss
B. Training accuracy
C. Feature scaling
D. Epochs
Answer: A - Which of the following models is most interpretable?
A. Neural Networks
B. Random Forest
C. Decision Tree
D. XGBoost
Answer: C
Mastering these Machine Learning MCQs is a valuable step toward building strong analytical thinking and model-building skills.
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