MACHINE LEARNING MCQS
Supervised learning requires:Unlabeled dataLabeled dataNo dataOnly reinforcement signalsB) Labeled dataSupervised learning uses labeled datasets to train models.
Which algorithm is used for classification?K-meansKNNAprioriDBSCANB) KNNK-Nearest Neighbor is a popular classification algorithm.
Overfitting in ML means:Model performs poorly on training dataModel performs well on training data but poorly on test dataModel generalizes wellModel underfits dataB) Model performs well on training data but poorly on test dataOverfitting occurs when a model memorizes training data and fails to generalize.
Which technique is used to avoid overfitting?RegularizationOvertrainingNoise AdditionIgnoring validation dataA) RegularizationRegularization adds penalty to complex models to avoid overfitting.
Gradient Descent is used to:Maximize lossMinimize lossStop trainingCreate dataB) Minimize lossGradient Descent updates model parameters to minimize the loss function.
Which of the following is a supervised learning algorithm?K-Means ClusteringLinear RegressionApriori AlgorithmPCAB) Linear RegressionLinear Regression is a supervised algorithm used to predict continuous values.
Overfitting in machine learning happens when:Model is too simpleModel performs poorly on training dataModel learns noise and performs poorly on new dataDataset is too largeC) Model learns noise and performs poorly on new dataOverfitting occurs when a model memorizes training data instead of generalizing patterns.
In classification tasks, confusion matrix is used to measure:Execution timeAccuracy and error typesTraining costModel complexityB) Accuracy and error typesConfusion matrix shows TP, FP, TN, FN to evaluate classification performance.
Which machine learning algorithm is based on the concept of "margin maximization"?Decision TreesNaïve BayesSupport Vector MachinesRandom ForestC) Support Vector MachinesSVM maximizes the margin between classes to achieve better classification boundaries.
Which technique is used for dimensionality reduction?KNNPCABaggingBoostingB) PCAPrincipal Component Analysis (PCA) reduces feature dimensions while preserving variance.
Which of the following is used to avoid overfitting?Increasing model complexityUsing regularizationReducing training dataRemoving validation setB) Using regularizationRegularization techniques like L1/L2 add penalties to model complexity to avoid overfitting.
Bagging stands for:Bag AggregationBoosted AggregationBootstrap AggregationBinary AggregationC) Bootstrap AggregationBagging trains multiple models on random samples and combines their predictions to reduce variance.
Which metric is preferred for imbalanced classification datasets?AccuracyPrecision-Recall or F1-ScoreExecution TimeMean Squared ErrorB) Precision-Recall or F1-ScoreF1-Score balances precision and recall, making it suitable for imbalanced data.
Gradient Descent is used for:Sorting DataMinimizing Loss FunctionIncreasing Model ComplexitySampling DataB) Minimizing Loss FunctionGradient Descent updates parameters in the direction that reduces the cost function.
Which algorithm uses ensemble of decision trees?Naive BayesLogistic RegressionRandom ForestLinear SVM C) Random ForestRandom Forest combines multiple decision trees to improve accuracy and reduce overfitting.