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{
"title": "Logistic Regression Mastery: 100 MCQs",
"description": "A comprehensive set of 100 multiple-choice questions designed to teach and test your understanding of Logistic Regression, from basic concepts to advanced topics like regularization, odds ratio, decision boundaries, and real-world scenario applications.",
"questions": [
{
"id": 1,
"questionText": "What is the main purpose of Logistic Regression?",
"options": [
"To cluster data points",
"To predict binary or categorical outcomes",
"To reduce dimensionality",
"To predict a continuous outcome"
],
"correctAnswerIndex": 1,
"explanation": "Logistic Regression models the probability of binary or categorical outcomes, not continuous values."
},
{
"id": 2,
"questionText": "Which function does Logistic Regression use to map predicted values to probabilities?",
"options": [
"Sigmoid function",
"ReLU function",
"Step function",
"Linear function"
],
"correctAnswerIndex": 0,
"explanation": "The sigmoid function maps any real-valued number into the range 0 to 1, representing probabilities."
},
{
"id": 3,
"questionText": "In Logistic Regression, what does the odds ratio represent?",
"options": [
"The number of features in the model",
"The error of the model",
"The predicted probability itself",
"The ratio of probability of success to failure"
],
"correctAnswerIndex": 3,
"explanation": "Odds ratio = probability of success / probability of failure."
},
{
"id": 4,
"questionText": "What type of relationship does Logistic Regression model between input and output?",
"options": [
"Non-linear relationship between input and output",
"Linear relationship between input and output",
"Linear relationship between input and probability log-odds",
"Polynomial relationship"
],
"correctAnswerIndex": 2,
"explanation": "Logistic Regression models the log-odds (logit) as a linear combination of inputs."
},
{
"id": 5,
"questionText": "Which loss function is used in Logistic Regression?",
"options": [
"Hinge Loss",
"Absolute Error",
"Mean Squared Error",
"Cross-Entropy / Log Loss"
],
"correctAnswerIndex": 3,
"explanation": "Logistic Regression uses log loss (cross-entropy) to penalize incorrect probabilistic predictions."
},
{
"id": 6,
"questionText": "Scenario: A dataset has highly imbalanced classes. What is a good approach in Logistic Regression?",
"options": [
"Remove majority class",
"Use class weights or resampling techniques",
"Ignore imbalance and train directly",
"Use Linear Regression instead"
],
"correctAnswerIndex": 1,
"explanation": "Class imbalance can bias predictions; weighting or resampling helps model performance."
},
{
"id": 7,
"questionText": "Which metric is most appropriate for evaluating Logistic Regression?",
"options": [
"Mean Absolute Error",
"Accuracy, Precision, Recall, F1-score",
"R-squared",
"Explained Variance"
],
"correctAnswerIndex": 1,
"explanation": "Classification metrics like accuracy, precision, recall, and F1-score are used for Logistic Regression."
},
{
"id": 8,
"questionText": "Scenario: Logistic Regression model shows overfitting. Recommended solution?",
"options": [
"Reduce dataset size",
"Apply regularization (L1 or L2)",
"Increase learning rate",
"Remove all features"
],
"correctAnswerIndex": 1,
"explanation": "Regularization penalizes large coefficients, reducing overfitting."
},
{
"id": 9,
"questionText": "Which regularization is used in Logistic Regression to encourage sparsity in coefficients?",
"options": [
"L2 Regularization (Ridge)",
"No regularization",
"L1 Regularization (Lasso)",
"ElasticNet only"
],
"correctAnswerIndex": 2,
"explanation": "L1 regularization encourages some coefficients to become exactly zero, promoting sparsity."
},
{
"id": 10,
"questionText": "Scenario: Logistic Regression is applied with two highly correlated features. Observation?",
"options": [
"Features ignored automatically",
"Model always underfits",
"Multicollinearity can inflate coefficient variance",
"Coefficients are exact"
],
"correctAnswerIndex": 2,
"explanation": "Highly correlated features lead to unstable coefficients due to multicollinearity."
},
{
"id": 11,
"questionText": "Which threshold is commonly used to convert probabilities into class predictions?",
"options": [
"1.0",
"Negative values",
"0.25",
"0.5"
],
"correctAnswerIndex": 3,
"explanation": "By default, probabilities ≥0.5 are classified as 1, below 0.5 as 0."
},
{
"id": 12,
"questionText": "Scenario: Predicted probability = 0.8. What is predicted class (threshold=0.5)?",
"options": [
"Depends on dataset",
"Undetermined",
"Class 1",
"Class 0"
],
"correctAnswerIndex": 2,
"explanation": "Probability >0.5 is classified as Class 1 by default."
},
{
"id": 13,
"questionText": "Scenario: Logistic Regression applied to a dataset with 3 classes. Which extension is required?",
"options": [
"Binary Logistic Regression",
"Ridge Regression",
"Linear Regression",
"Multinomial Logistic Regression (Softmax)"
],
"correctAnswerIndex": 3,
"explanation": "Multinomial logistic regression generalizes to multi-class problems using softmax function."
},
{
"id": 14,
"questionText": "What does the coefficient in Logistic Regression represent?",
"options": [
"Predicted probability",
"Change in log-odds per unit change in feature",
"Mean of feature",
"Error term"
],
"correctAnswerIndex": 1,
"explanation": "Each coefficient measures the impact of its feature on log-odds of the outcome."
},
{
"id": 15,
"questionText": "Scenario: Logistic Regression is applied with L2 regularization. Observation?",
"options": [
"Coefficients become exactly zero",
"Model ignores features",
"Training fails",
"Coefficients shrink, helps reduce overfitting"
],
"correctAnswerIndex": 3,
"explanation": "L2 penalizes large coefficients, reducing variance without forcing zeros."
},
{
"id": 16,
"questionText": "Which function converts log-odds to probability in Logistic Regression?",
"options": [
"ReLU",
"Linear",
"Tanh",
"Sigmoid"
],
"correctAnswerIndex": 3,
"explanation": "Sigmoid maps log-odds to probability between 0 and 1."
},
{
"id": 17,
"questionText": "Scenario: Dataset has 100 features, 10,000 samples. Regularization needed?",
"options": [
"No, model will generalize automatically",
"Yes, to prevent overfitting and reduce coefficient variance",
"Remove samples instead",
"Use only linear regression"
],
"correctAnswerIndex": 1,
"explanation": "Regularization is important when features are many relative to samples to improve generalization."
},
{
"id": 18,
"questionText": "Scenario: Logistic Regression shows poor recall for minority class. Solution?",
"options": [
"Remove majority class",
"Ignore minority class",
"Increase learning rate",
"Adjust decision threshold or use class weights"
],
"correctAnswerIndex": 3,
"explanation": "Threshold adjustment or class weighting helps improve minority class prediction."
},
{
"id": 19,
"questionText": "Which optimization method is commonly used to train Logistic Regression?",
"options": [
"Random Forest",
"Gradient Descent / Newton-Raphson",
"PCA",
"K-Means"
],
"correctAnswerIndex": 1,
"explanation": "Gradient-based optimization (like gradient descent or Newton-Raphson) is used to minimize log-loss."
},
{
"id": 20,
"questionText": "Scenario: Logistic Regression applied with perfect separation. Observation?",
"options": [
"Model ignores features",
"Coefficients can go to infinity; regularization needed",
"Model works fine without issues",
"Training error is high"
],
"correctAnswerIndex": 1,
"explanation": "Perfect separation leads to extremely large coefficients; L1/L2 regularization stabilizes estimates."
},
{
"id": 21,
"questionText": "Scenario: Logistic Regression applied to highly imbalanced dataset. Metric to monitor?",
"options": [
"R-squared",
"Precision, Recall, F1-score",
"Explained Variance",
"Mean Absolute Error"
],
"correctAnswerIndex": 1,
"explanation": "Classification metrics like precision, recall, and F1 are more appropriate than regression metrics."
},
{
"id": 22,
"questionText": "Scenario: Model predicts 0.49 for minority class with threshold=0.5. Observation?",
"options": [
"Prediction invalid",
"Class predicted as 0; threshold can be adjusted",
"Model underfits",
"Class predicted as 1"
],
"correctAnswerIndex": 1,
"explanation": "Probability <0.5 leads to class 0; threshold adjustment can improve minority class recall."
},
{
"id": 23,
"questionText": "Scenario: Logistic Regression with correlated inputs. Potential issue?",
"options": [
"Model ignores correlated features automatically",
"Multicollinearity inflates variance of coefficients",
"Training fails",
"Model underfits"
],
"correctAnswerIndex": 1,
"explanation": "Multicollinearity leads to unstable coefficient estimates."
},
{
"id": 24,
"questionText": "Scenario: Logistic Regression used for spam email detection. What is the output?",
"options": [
"Continuous score unrelated to probability",
"Distance from origin",
"Probability of spam",
"Exact class label only"
],
"correctAnswerIndex": 2,
"explanation": "Logistic Regression outputs the probability of the positive class (spam)."
},
{
"id": 25,
"questionText": "Scenario: Logistic Regression applied with L1 regularization. Observation?",
"options": [
"Training fails",
"Model ignores features",
"Some coefficients may become exactly zero, feature selection happens",
"All coefficients increase"
],
"correctAnswerIndex": 2,
"explanation": "L1 regularization shrinks some coefficients to zero, effectively performing feature selection."
},
{
"id": 26,
"questionText": "Scenario: Logistic Regression applied to dataset with outliers. Observation?",
"options": [
"Model underfits",
"Training fails",
"Outliers have no effect",
"Coefficients may be skewed by outliers"
],
"correctAnswerIndex": 3,
"explanation": "Outliers can distort the logistic regression coefficients, affecting predictions."
},
{
"id": 27,
"questionText": "Scenario: Logistic Regression with L2 regularization on small dataset. Observation?",
"options": [
"Model ignores features",
"Coefficients become exactly zero",
"Coefficients shrink, improving generalization",
"Training fails"
],
"correctAnswerIndex": 2,
"explanation": "L2 regularization penalizes large coefficients, stabilizing them for small datasets."
},
{
"id": 28,
"questionText": "Scenario: Logistic Regression applied with highly correlated features. Observation?",
"options": [
"Model underfits",
"Model ignores correlated features automatically",
"Training fails",
"Multicollinearity inflates variance of coefficients"
],
"correctAnswerIndex": 3,
"explanation": "Highly correlated features lead to unstable coefficient estimates, increasing variance."
},
{
"id": 29,
"questionText": "Scenario: Logistic Regression used for credit default prediction. Output?",
"options": [
"Distance from origin",
"Exact class label only",
"Continuous score unrelated to probability",
"Probability of default"
],
"correctAnswerIndex": 3,
"explanation": "The model outputs probabilities, which can then be converted to class labels using a threshold."
},
{
"id": 30,
"questionText": "Scenario: Logistic Regression trained with balanced class weights. Observation?",
"options": [
"All probabilities are 0.5",
"Model ignores minority class",
"Minority class predictions improve",
"Training fails"
],
"correctAnswerIndex": 2,
"explanation": "Class weights balance the loss function, improving minority class prediction."
},
{
"id": 31,
"questionText": "Scenario: Logistic Regression applied with feature scaling. Observation?",
"options": [
"Scaling changes predicted classes",
"Scaling reduces number of features",
"Scaling is required to make model work",
"Scaling helps optimization but does not affect model predictions"
],
"correctAnswerIndex": 3,
"explanation": "Feature scaling speeds up convergence but does not change final probabilities."
},
{
"id": 32,
"questionText": "Scenario: Logistic Regression applied with perfect separation. Observation?",
"options": [
"Coefficients may become infinite",
"Training fails automatically",
"Model ignores features",
"Model underfits"
],
"correctAnswerIndex": 0,
"explanation": "Perfect separation leads to very large coefficients; regularization stabilizes estimates."
},
{
"id": 33,
"questionText": "Scenario: Logistic Regression applied with threshold=0.7. Observation?",
"options": [
"Predictions become stricter for positive class",
"Model underfits",
"Threshold does not affect predictions",
"Predictions become more lenient"
],
"correctAnswerIndex": 0,
"explanation": "Higher threshold means a higher probability is required to classify as positive, reducing false positives but increasing false negatives."
},
{
"id": 34,
"questionText": "Scenario: Logistic Regression applied with L1 regularization. Observation?",
"options": [
"Training fails",
"All coefficients increase",
"Some coefficients shrink to zero",
"Model ignores features"
],
"correctAnswerIndex": 2,
"explanation": "L1 encourages sparsity; some features are removed automatically."
},
{
"id": 35,
"questionText": "Scenario: Logistic Regression applied to multiclass problem. Observation?",
"options": [
"Binary logistic regression works fine",
"Model ignores extra classes",
"Use multinomial logistic regression with softmax",
"Training fails"
],
"correctAnswerIndex": 2,
"explanation": "Binary logistic regression cannot handle more than two classes without modification."
},
{
"id": 36,
"questionText": "Scenario: Logistic Regression applied to imbalanced dataset. Observation?",
"options": [
"Minority class predictions improve automatically",
"Majority class dominates predictions",
"Training fails",
"Model ignores majority class"
],
"correctAnswerIndex": 1,
"explanation": "Without adjustments, logistic regression may predict majority class most of the time."
},
{
"id": 37,
"questionText": "Scenario: Logistic Regression applied with gradient descent optimizer. Observation?",
"options": [
"Coefficients are updated iteratively to minimize log-loss",
"Training fails",
"Model overfits automatically",
"Predictions remain constant"
],
"correctAnswerIndex": 0,
"explanation": "Gradient descent iteratively updates weights to minimize cross-entropy loss."
},
{
"id": 38,
"questionText": "Scenario: Logistic Regression applied to dataset with categorical features. Observation?",
"options": [
"Model ignores categorical features",
"Categorical features must be encoded (e.g., one-hot)",
"Training fails",
"Model handles categories directly"
],
"correctAnswerIndex": 1,
"explanation": "Logistic Regression requires numeric input; categorical variables must be encoded."
},
{
"id": 39,
"questionText": "Scenario: Logistic Regression applied with very few samples. Observation?",
"options": [
"Training fails",
"Model ignores features",
"Regularization is critical to prevent overfitting",
"Model always underfits"
],
"correctAnswerIndex": 2,
"explanation": "Small datasets can lead to high variance; regularization helps stabilize coefficients."
},
{
"id": 40,
"questionText": "Scenario: Logistic Regression applied to text classification using TF-IDF features. Observation?",
"options": [
"Training error is zero",
"Model fails automatically",
"Model can handle high-dimensional sparse data with regularization",
"Model ignores sparse features"
],
"correctAnswerIndex": 2,
"explanation": "With regularization, logistic regression works well on high-dimensional sparse data like TF-IDF vectors."
},
{
"id": 41,
"questionText": "Scenario: Logistic Regression applied to dataset with missing values. Observation?",
"options": [
"Training fails",
"Imputation required before training",
"Model ignores missing values automatically",
"Model underfits"
],
"correctAnswerIndex": 1,
"explanation": "Logistic Regression cannot handle missing values directly; preprocessing like imputation is required."
},
{
"id": 42,
"questionText": "Scenario: Logistic Regression applied with regularization strength very high. Observation?",
"options": [
"Training fails",
"Model ignores features",
"Coefficients increase automatically",
"Coefficients shrink drastically, model may underfit"
],
"correctAnswerIndex": 3,
"explanation": "High regularization penalizes coefficients heavily, potentially underfitting the data."
},
{
"id": 43,
"questionText": "Scenario: Logistic Regression applied with learning rate too high. Observation?",
"options": [
"Optimization may diverge",
"Training fails silently",
"Predictions remain perfect",
"Model always converges"
],
"correctAnswerIndex": 0,
"explanation": "Too high learning rate can make gradient descent overshoot, preventing convergence."
},
{
"id": 44,
"questionText": "Scenario: Logistic Regression applied with L1 and L2 regularization combined. Observation?",
"options": [
"Training fails",
"ElasticNet combines both, balancing sparsity and coefficient shrinkage",
"All coefficients go to zero",
"Model ignores features"
],
"correctAnswerIndex": 1,
"explanation": "ElasticNet uses a weighted combination of L1 and L2 to balance sparsity and shrinkage."
},
{
"id": 45,
"questionText": "Scenario: Logistic Regression applied with non-linear patterns in features. Observation?",
"options": [
"Model ignores non-linear features",
"Training fails",
"Model captures non-linearities automatically",
"Linear decision boundary may underfit; feature engineering or polynomial expansion needed"
],
"correctAnswerIndex": 3,
"explanation": "Logistic Regression assumes linear relationship between log-odds and features; non-linearities require feature transformations."
},
{
"id": 46,
"questionText": "Scenario: Logistic Regression applied to probability output. Which method can calibrate probabilities?",
"options": [
"StandardScaler",
"PCA",
"Platt Scaling or Isotonic Regression",
"Ridge Regression"
],
"correctAnswerIndex": 2,
"explanation": "Platt scaling or isotonic regression adjusts predicted probabilities for better calibration."
},
{
"id": 47,
"questionText": "Scenario: Logistic Regression applied with many irrelevant features. Observation?",
"options": [
"Training fails",
"Regularization can reduce effect of irrelevant features",
"Model ignores irrelevant features automatically",
"Model overfits regardless"
],
"correctAnswerIndex": 1,
"explanation": "Regularization reduces coefficients of uninformative features, improving generalization."
},
{
"id": 48,
"questionText": "Scenario: Logistic Regression applied to dataset with 3 classes. Threshold method?",
"options": [
"Model fails",
"Softmax probabilities used for multiclass prediction",
"Binary logistic regression applies",
"Single threshold 0.5 used for all classes"
],
"correctAnswerIndex": 1,
"explanation": "Multinomial logistic regression uses softmax to handle multiple classes."
},
{
"id": 49,
"questionText": "Scenario: Logistic Regression applied to dataset with skewed classes. Observation?",
"options": [
"Minority class ignored automatically",
"Class weighting or resampling improves minority prediction",
"Model fails",
"All predictions become majority class"
],
"correctAnswerIndex": 1,
"explanation": "Class weighting or resampling is needed to handle skewed datasets effectively."
},
{
"id": 50,
"questionText": "Scenario: Logistic Regression applied with early stopping during optimization. Observation?",
"options": [
"Coefficients go to zero",
"Model always underfits",
"Prevents overfitting and reduces training time",
"Training fails"
],
"correctAnswerIndex": 2,
"explanation": "Early stopping halts training when improvement slows, helping avoid overfitting."
},
{
"id": 51,
"questionText": "Scenario: Logistic Regression applied to dataset with nonlinear boundaries. Observation?",
"options": [
"Model perfectly separates classes",
"Training fails",
"Model ignores features",
"Linear decision boundary may underfit; feature engineering needed"
],
"correctAnswerIndex": 3,
"explanation": "Logistic Regression assumes linear decision boundary on log-odds; nonlinear relationships require transformations."
},
{
"id": 52,
"questionText": "Scenario: Logistic Regression applied to high-dimensional sparse data like text. Observation?",
"options": [
"Training fails automatically",
"Model ignores sparse features",
"Model always underfits",
"Regularization is essential to prevent overfitting"
],
"correctAnswerIndex": 3,
"explanation": "L1 or L2 regularization stabilizes coefficients in high-dimensional sparse datasets."
},
{
"id": 53,
"questionText": "Scenario: Logistic Regression applied to highly imbalanced dataset. Best practice?",
"options": [
"Increase learning rate",
"Use class weighting or resampling techniques",
"Ignore imbalance and train directly",
"Remove minority class"
],
"correctAnswerIndex": 1,
"explanation": "Adjusting for class imbalance helps improve minority class predictions."
},
{
"id": 54,
"questionText": "Scenario: Logistic Regression model shows large coefficients for correlated features. Observation?",
"options": [
"Model ignores correlated features automatically",
"Coefficients are perfect",
"Training fails",
"Multicollinearity inflates variance of coefficients"
],
"correctAnswerIndex": 3,
"explanation": "Highly correlated inputs can lead to unstable coefficient estimates."
},
{
"id": 55,
"questionText": "Scenario: Logistic Regression applied with threshold=0.3. Observation?",
"options": [
"Threshold has no effect",
"Predictions become stricter",
"Model underfits",
"Predictions become more lenient for positive class"
],
"correctAnswerIndex": 3,
"explanation": "Lowering threshold increases positive predictions, improving recall but may reduce precision."
},
{
"id": 56,
"questionText": "Scenario: Logistic Regression applied with very small L2 regularization. Observation?",
"options": [
"Training fails",
"Model underfits automatically",
"Coefficients shrink to zero",
"Coefficients may be large, risk of overfitting"
],
"correctAnswerIndex": 3,
"explanation": "Small regularization may allow large coefficients, increasing variance."
},
{
"id": 57,
"questionText": "Scenario: Logistic Regression applied with L1 regularization. Observation?",
"options": [
"Some coefficients shrink to zero, performing feature selection",
"Training fails",
"All coefficients increase",
"Model ignores features"
],
"correctAnswerIndex": 0,
"explanation": "L1 regularization promotes sparsity, setting some coefficients exactly to zero."
},
{
"id": 58,
"questionText": "Scenario: Logistic Regression applied to multiclass problem. Observation?",
"options": [
"Training fails",
"Binary logistic regression works fine",
"Use multinomial logistic regression with softmax",
"Model ignores extra classes"
],
"correctAnswerIndex": 2,
"explanation": "Binary logistic regression cannot handle more than two classes without modification."
},
{
"id": 59,
"questionText": "Scenario: Logistic Regression applied to dataset with missing values. Observation?",
"options": [
"Training fails automatically",
"Model ignores missing values automatically",
"Model underfits",
"Imputation required before training"
],
"correctAnswerIndex": 3,
"explanation": "Logistic Regression cannot handle missing values directly; preprocessing is needed."
},
{
"id": 60,
"questionText": "Scenario: Logistic Regression applied to text classification with TF-IDF features. Observation?",
"options": [
"Training error is zero",
"Model fails automatically",
"All sparse features are ignored",
"Regularization prevents overfitting in high-dimensional sparse features"
],
"correctAnswerIndex": 3,
"explanation": "Regularization stabilizes coefficients and improves generalization on sparse datasets."
},
{
"id": 61,
"questionText": "Scenario: Logistic Regression applied with gradient descent and large learning rate. Observation?",
"options": [
"Model converges perfectly",
"Optimization may diverge",
"Model ignores features",
"Predictions remain constant"
],
"correctAnswerIndex": 1,
"explanation": "Too high learning rate can cause gradient descent to overshoot and fail to converge."
},
{
"id": 62,
"questionText": "Scenario: Logistic Regression applied with perfect separation in classes. Observation?",
"options": [
"Model underfits",
"Training fails automatically",
"Coefficients may become extremely large",
"Model ignores features"
],
"correctAnswerIndex": 2,
"explanation": "Perfect separation leads to very large coefficients; regularization helps stabilize the model."
},
{
"id": 63,
"questionText": "Scenario: Logistic Regression applied with early stopping. Observation?",
"options": [
"Training fails",
"Coefficients go to zero",
"Prevents overfitting and reduces training time",
"Model always underfits"
],
"correctAnswerIndex": 2,
"explanation": "Early stopping halts training when loss improvement slows, improving generalization."
},
{
"id": 64,
"questionText": "Scenario: Logistic Regression applied to dataset with skewed target. Observation?",
"options": [
"Use class weights or resampling to balance predictions",
"Minority class ignored automatically",
"All predictions become majority class",
"Model fails"
],
"correctAnswerIndex": 0,
"explanation": "Adjusting for skewed targets helps prevent biased predictions toward majority class."
},
{
"id": 65,
"questionText": "Scenario: Logistic Regression applied with categorical features. Observation?",
"options": [
"Model ignores categorical features",
"Training fails",
"Categorical features must be encoded numerically",
"Model handles categories automatically"
],
"correctAnswerIndex": 2,
"explanation": "Logistic Regression requires numeric input, so categories need encoding (e.g., one-hot)."
},
{
"id": 66,
"questionText": "Scenario: Logistic Regression applied with too many irrelevant features. Observation?",
"options": [
"Model ignores irrelevant features automatically",
"Model overfits regardless",
"Training fails",
"Regularization reduces effect of irrelevant features"
],
"correctAnswerIndex": 3,
"explanation": "Regularization helps suppress coefficients of uninformative features."
},
{
"id": 67,
"questionText": "Scenario: Logistic Regression applied with L1 and L2 combined. Observation?",
"options": [
"ElasticNet balances sparsity and shrinkage",
"All coefficients become zero",
"Model ignores features",
"Training fails"
],
"correctAnswerIndex": 0,
"explanation": "ElasticNet combines L1 and L2 penalties to balance feature selection and coefficient shrinkage."
},
{
"id": 68,
"questionText": "Scenario: Logistic Regression applied with adjusted threshold for minority class. Observation?",
"options": [
"Precision decreases automatically",
"Recall of minority class improves",
"All predictions become majority class",
"Model fails"
],
"correctAnswerIndex": 1,
"explanation": "Lowering threshold increases positive predictions, improving recall for minority class."
},
{
"id": 69,
"questionText": "Scenario: Logistic Regression applied with small dataset. Observation?",
"options": [
"Model underfits automatically",
"Training fails",
"Regularization stabilizes coefficients and reduces variance",
"Model ignores features"
],
"correctAnswerIndex": 2,
"explanation": "Small datasets are prone to overfitting; regularization improves generalization."
},
{
"id": 70,
"questionText": "Scenario: Logistic Regression applied with non-linear feature transformations. Observation?",
"options": [
"Training fails",
"Model ignores non-linear features",
"Non-linear terms help model complex relationships",
"Predictions remain linear"
],
"correctAnswerIndex": 2,
"explanation": "Polynomial or interaction terms allow Logistic Regression to capture non-linear relationships."
},
{
"id": 71,
"questionText": "Scenario: Logistic Regression applied with continuous target mistakenly. Observation?",
"options": [
"Model ignores continuous targets",
"Model works fine",
"Training fails",
"Logistic Regression is inappropriate; should use Linear Regression"
],
"correctAnswerIndex": 3,
"explanation": "Logistic Regression predicts probabilities for categorical outcomes, not continuous values."
},
{
"id": 72,
"questionText": "Scenario: Logistic Regression applied with L2 regularization too strong. Observation?",
"options": [
"Training fails",
"Model ignores features",
"Coefficients increase automatically",
"Model may underfit due to overly shrunk coefficients"
],
"correctAnswerIndex": 3,
"explanation": "Excessive regularization reduces coefficient magnitude, potentially underfitting."
},
{
"id": 73,
"questionText": "Scenario: Logistic Regression applied to imbalanced multiclass problem. Observation?",
"options": [
"Training fails",
"Class weighting or resampling recommended for each class",
"All predictions go to majority class",
"Model ignores minority classes"
],
"correctAnswerIndex": 1,
"explanation": "Balanced weighting improves prediction performance for minority classes."
},
{
"id": 74,
"questionText": "Scenario: Logistic Regression applied with very high learning rate. Observation?",
"options": [
"Gradient descent may diverge",
"Model ignores features",
"Predictions remain constant",
"Model converges perfectly"
],
"correctAnswerIndex": 0,
"explanation": "Too high learning rate causes optimization to overshoot, preventing convergence."
},
{
"id": 75,
"questionText": "Scenario: Logistic Regression applied with probability calibration methods. Observation?",
"options": [
"Training fails",
"Calibration has no effect",
"Platt scaling or isotonic regression improves predicted probabilities",
"Model ignores calibration"
],
"correctAnswerIndex": 2,
"explanation": "Probability calibration aligns predicted probabilities with true outcomes, improving reliability."
},
{
"id": 76,
"questionText": "Scenario: Logistic Regression applied with small training data and no regularization. Observation?",
"options": [
"Model may overfit due to high variance",
"Training fails",
"Model underfits automatically",
"Model ignores features"
],
"correctAnswerIndex": 0,
"explanation": "Small datasets can cause overfitting; regularization helps stabilize coefficients."
},
{
"id": 77,
"questionText": "Scenario: Logistic Regression applied with a feature highly correlated with target. Observation?",
"options": [
"Model ignores the feature",
"Training fails",
"Model coefficient will likely be significant",
"Regularization removes feature automatically"
],
"correctAnswerIndex": 2,
"explanation": "Highly predictive features typically get larger coefficients, unless heavily regularized."
},
{
"id": 78,
"questionText": "Scenario: Logistic Regression applied with overcomplete features (more features than samples). Observation?",
"options": [
"Training fails automatically",
"Regularization is essential to prevent overfitting",
"Model always underfits",
"All features ignored"
],
"correctAnswerIndex": 1,
"explanation": "Too many features relative to samples increase overfitting risk; regularization stabilizes model."
},
{
"id": 79,
"questionText": "Scenario: Logistic Regression applied with extreme class imbalance. Observation?",
"options": [
"Minority class predictions improve automatically",
"Predictions dominated by majority class without class weighting",
"Training fails",
"All probabilities become 0.5"
],
"correctAnswerIndex": 1,
"explanation": "Without adjustments, the model predicts the majority class most of the time."
},
{
"id": 80,
"questionText": "Scenario: Logistic Regression applied to multiclass problem using one-vs-rest. Observation?",
"options": [
"Binary logistic regression fails automatically",
"Training fails",
"Each class is treated as positive against all others",
"Only majority class is predicted"
],
"correctAnswerIndex": 2,
"explanation": "One-vs-rest handles multiclass by training separate classifiers for each class."
},
{
"id": 81,
"questionText": "Scenario: Logistic Regression applied with very high regularization. Observation?",
"options": [
"Predictions become perfect",
"Coefficients shrink too much; model may underfit",
"Coefficients increase automatically",
"Training fails"
],
"correctAnswerIndex": 1,
"explanation": "Strong regularization reduces coefficient magnitude excessively, potentially underfitting."
},
{
"id": 82,
"questionText": "Scenario: Logistic Regression applied with a categorical feature incorrectly encoded as ordinal. Observation?",
"options": [
"Model ignores feature automatically",
"Training fails",
"Model may misinterpret ordering; predictions may be biased",
"Predictions remain correct"
],
"correctAnswerIndex": 2,
"explanation": "Ordinal encoding imposes an artificial order; one-hot encoding is better for nominal features."
},
{
"id": 83,
"questionText": "Scenario: Logistic Regression applied with overlapping class distributions. Observation?",
"options": [
"Training fails",
"Model may have misclassifications; probabilities indicate uncertainty",
"Model ignores overlapping features",
"All predictions are correct"
],
"correctAnswerIndex": 1,
"explanation": "Overlap leads to inherent classification errors; logistic regression outputs probability estimates reflecting uncertainty."
},
{
"id": 84,
"questionText": "Scenario: Logistic Regression applied with threshold set very high (0.9). Observation?",
"options": [
"Few positives predicted; recall decreases",
"Training fails",
"All predictions become positive",
"Model underfits automatically"
],
"correctAnswerIndex": 0,
"explanation": "High threshold reduces positive predictions, improving precision but lowering recall."
},
{
"id": 85,
"questionText": "Scenario: Logistic Regression applied with L1 regularization on sparse dataset. Observation?",
"options": [
"All coefficients increase",
"Model ignores sparse features",
"Training fails automatically",
"Some coefficients shrink to zero, performing feature selection"
],
"correctAnswerIndex": 3,
"explanation": "L1 encourages sparsity, zeroing out uninformative features."
},
{
"id": 86,
"questionText": "Scenario: Logistic Regression applied with feature scaling not applied. Observation?",
"options": [
"Model fails automatically",
"Optimization may be slower but predictions unaffected",
"Predictions become invalid",
"Coefficients ignored"
],
"correctAnswerIndex": 1,
"explanation": "Scaling affects optimization speed, not the final probability outputs."
},
{
"id": 87,
"questionText": "Scenario: Logistic Regression applied with learning rate too low. Observation?",
"options": [
"Predictions remain constant",
"Training fails",
"Model underfits automatically",
"Convergence is slow but eventual solution correct"
],
"correctAnswerIndex": 3,
"explanation": "Small learning rate slows gradient descent but does not prevent eventual convergence."
},
{
"id": 88,
"questionText": "Scenario: Logistic Regression applied to dataset with multicollinearity. Observation?",
"options": [
"Coefficients unstable; variance inflated",
"Training fails",
"Model ignores correlated features automatically",
"Predictions unaffected"
],
"correctAnswerIndex": 0,
"explanation": "High correlation among features inflates coefficient variance, making estimates unstable."
},
{
"id": 89,
"questionText": "Scenario: Logistic Regression applied with probability calibration. Observation?",
"options": [
"Platt scaling or isotonic regression improves probability estimates",
"Calibration has no effect",
"Training fails",
"Model ignores calibration"
],
"correctAnswerIndex": 0,
"explanation": "Probability calibration aligns predicted probabilities with actual outcomes."
},
{
"id": 90,
"questionText": "Scenario: Logistic Regression applied with interaction terms added. Observation?",
"options": [
"Training fails",
"Model can capture combined effect of features",
"Model ignores interactions",
"Predictions become random"
],
"correctAnswerIndex": 1,
"explanation": "Interaction terms allow logistic regression to model dependencies between features."
},
{
"id": 91,
"questionText": "Scenario: Logistic Regression applied to dataset with outliers. Observation?",
"options": [
"Training fails",
"Predictions unaffected",
"Model ignores outliers automatically",
"Outliers may distort coefficients; regularization helps"
],
"correctAnswerIndex": 3,
"explanation": "Outliers can skew estimates; regularization stabilizes coefficients."
},
{
"id": 92,
"questionText": "Scenario: Logistic Regression applied with small sample size and large number of features. Observation?",
"options": [
"Model underfits automatically",
"High risk of overfitting; regularization essential",
"Predictions remain perfect",
"Training fails"
],
"correctAnswerIndex": 1,
"explanation": "Many features relative to samples increase variance; regularization prevents overfitting."
},
{
"id": 93,
"questionText": "Scenario: Logistic Regression applied with multiclass softmax. Observation?",
"options": [
"Model ignores extra classes",
"Training fails",
"Binary thresholding works automatically",
"Softmax outputs probabilities for each class"
],
"correctAnswerIndex": 3,
"explanation": "Softmax generalizes logistic regression to multiple classes, outputting probabilities."
},
{
"id": 94,
"questionText": "Scenario: Logistic Regression applied with polynomial features. Observation?",
"options": [
"Predictions remain linear",
"Model ignores polynomial terms",
"Training fails",
"Can model non-linear relationships between features"
],
"correctAnswerIndex": 3,
"explanation": "Polynomial terms allow logistic regression to capture non-linear effects."
},
{
"id": 95,
"questionText": "Scenario: Logistic Regression applied with overfitting on training set. Observation?",
"options": [
"Training fails automatically",
"Model ignores training data",
"Predictions perfect on test set",
"Apply regularization or reduce features"
],
"correctAnswerIndex": 3,
"explanation": "Regularization or feature selection reduces overfitting and improves generalization."
},
{
"id": 96,
"questionText": "Scenario: Logistic Regression applied to dataset with skewed class distribution. Observation?",
"options": [
"Training fails",
"Predictions always majority class",
"Use class weights or resampling",
"Model ignores minority class automatically"
],
"correctAnswerIndex": 2,
"explanation": "Adjusting for imbalance improves minority class prediction performance."
},
{
"id": 97,
"questionText": "Scenario: Logistic Regression applied with continuous predictors on very different scales. Observation?",
"options": [
"Model fails automatically",
"Training error zero",
"Predictions invalid",
"Scaling helps optimization; predictions unchanged"
],
"correctAnswerIndex": 3,
"explanation": "Scaling speeds convergence but does not affect model predictions."
},
{
"id": 98,
"questionText": "Scenario: Logistic Regression applied with threshold adjustment. Observation?",
"options": [
"Threshold has no effect",
"Changing threshold trades off precision and recall",
"Training fails",
"Predictions remain constant"
],
"correctAnswerIndex": 1,
"explanation": "Adjusting threshold changes classification cutoff, affecting false positives and negatives."
},
{
"id": 99,
"questionText": "Scenario: Logistic Regression applied with noisy data. Observation?",
"options": [
"Noise is ignored automatically",
"Model may misclassify; regularization improves stability",
"Predictions perfect",
"Training fails"
],
"correctAnswerIndex": 1,
"explanation": "Noise affects coefficient estimation; regularization improves generalization."
},
{
"id": 100,
"questionText": "Scenario: Logistic Regression applied with missing categorical features. Observation?",
"options": [
"Model ignores missing categories automatically",
"Training fails",
"Predictions unaffected",
"Imputation or encoding needed before training"
],
"correctAnswerIndex": 3,
"explanation": "Missing categorical data must be imputed or encoded for logistic regression to work."
}
]
}
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