Back to Subject

Unit 2: Classification with scikit-learn - Practice Quiz

INT395 — Supervised Learning 49 Questions
0 Correct 0 Wrong 49 Left
0/49

1 In the context of supervised learning, what distinguishes a classification problem from a regression problem?

A. The input variables are continuous.
B. The target variable is categorical or discrete.
C. The target variable is continuous.
D. The training data is unlabeled.

2 Which scikit-learn method is primarily used to train a classifier on a dataset ?

A. model.score(X, y)
B. model.transform(X, y)
C. model.predict(X, y)
D. model.fit(X, y)

3 What is the standard shape of the input matrix expected by scikit-learn classifiers?

A. (n_features, n_classes)
B. (n_features, n_samples)
C. (n_samples, n_features)
D. (n_samples, n_samples)

4 Which of the following metrics is defined as the ratio of correctly predicted observations to the total observations?

A. F1-Score
B. Recall
C. Accuracy
D. Precision

5 In a Confusion Matrix, what does a False Positive (FP) represent?

A. The model predicted Positive, but the actual class was Negative.
B. The model predicted Negative, and the actual class was Positive.
C. The model predicted Negative, and the actual class was Negative.
D. The model predicted Positive, and the actual class was Positive.

6 Which metric is best suited for a classification problem where False Negatives are much more costly than False Positives (e.g., detecting a deadly disease)?

A. Recall
B. Precision
C. Accuracy
D. Specificity

7 Calculate the Precision given: , , .

A. 0.91
B. 0.50
C. 0.83
D. 0.20

8 The F1-Score is the harmonic mean of which two metrics?

A. Specificity and Sensitivity
B. Accuracy and Recall
C. Precision and Recall
D. TPR and FPR

9 In an ROC curve, the x-axis and y-axis represent which metrics respectively?

A. Recall vs Accuracy
B. True Negative Rate vs True Positive Rate
C. Precision vs Recall
D. False Positive Rate vs True Positive Rate

10 What does an AUC (Area Under Curve) score of 0.5 imply about a classifier?

A. It performs no better than random guessing.
B. It has zero errors.
C. It is a perfect classifier.
D. It predicts the negative class always.

11 When using classification_report in scikit-learn, what does the macro avg represent?

A. The weighted average based on support size.
B. The unweighted mean of the metric for each label.
C. The standard deviation of the metric.
D. The accuracy of the model.

12 Which issue makes Accuracy a misleading metric?

A. High computational cost.
B. It cannot be calculated for multiclass problems.
C. Linearly separable data.
D. Imbalanced datasets.

13 What is the activation function used in the standard Perceptron algorithm for binary classification?

A. ReLU function
B. Heaviside step function
C. Tanh function
D. Sigmoid function

14 The Perceptron algorithm is guaranteed to converge only if:

A. The learning rate is greater than 1.
B. The data is linearly separable.
C. The data is normally distributed.
D. The weights are initialized to zero.

15 Which function maps the output of a linear equation to a probability value in in Logistic Regression?

A. Softmax
B. Step
C. Sigmoid (Logistic)
D. Logarithm

16 The decision boundary generated by a standard Logistic Regression model is:

A. Irregular
B. Linear
C. Polynomial
D. Circular

17 In scikit-learn's LogisticRegression, what is the purpose of the parameter C?

A. It sets the number of iterations.
B. It controls the learning rate.
C. It determines the kernel type.
D. It is the inverse of regularization strength.

18 Which loss function is minimized in Logistic Regression?

A. Log Loss (Cross-Entropy)
B. Hinge Loss
C. Mean Squared Error
D. Gini Impurity

19 How does k-Nearest Neighbors (k-NN) classify a new data point?

A. By calculating the probability using Bayes' theorem.
B. By projecting the point onto a hyperplane.
C. By taking a majority vote of the closest training examples.
D. By finding the best splitting feature.

20 Why is k-NN often referred to as a lazy learner?

A. It only generalizes the data during the prediction phase.
B. It uses a simple distance metric.
C. It trains very slowly.
D. It ignores outliers.

21 In k-NN, what is the effect of choosing a very small value for (e.g., )?

A. The model becomes a linear classifier.
B. High Bias, Low Variance (Underfitting)
C. Low Bias, High Variance (Overfitting)
D. The decision boundary becomes smooth.

22 Which preprocessing step is critical for k-NN performance?

A. Increasing the number of features
B. One-hot encoding target labels
C. Removing correlations
D. Feature Scaling

23 Which distance metric is calculated as ?

A. Cosine Similarity
B. Manhattan Distance
C. Minkowski Distance
D. Euclidean Distance

24 In a Decision Tree, what does a leaf node represent?

A. A decision rule.
B. A class label or probability.
C. A feature to split on.
D. The root of the tree.

25 Which metric does the CART algorithm (used by scikit-learn for Decision Trees) use by default to measure impurity?

A. Gini Impurity
B. Entropy
C. Mean Squared Error
D. Log Loss

26 Calculate the Gini Impurity of a node containing 3 positive samples and 3 negative samples.

A. 0.25
B. 1.0
C. 0.5
D. 0.0

27 Which hyperparameter in DecisionTreeClassifier can be used to control overfitting?

A. max_depth
B. C
C. learning_rate
D. kernel

28 What is the concept of Information Gain in Decision Trees?

A. The time taken to train the tree.
B. The increase in accuracy after a split.
C. The total number of nodes in the tree.
D. The reduction in entropy (or impurity) achieved by a split.

29 Decision Trees split the feature space into regions using boundaries that are:

A. Curved
B. Diagonal
C. Circular
D. Orthogonal to the feature axes

30 The primary objective of a Support Vector Machine (SVM) is to find a hyperplane that:

A. Minimizes the number of support vectors.
B. Separates data with zero error regardless of margin.
C. Passes through the mean of the data.
D. Maximizes the margin between classes.

31 What are Support Vectors in SVM?

A. The data points furthest from the decision boundary.
B. The misclassified data points.
C. The data points closest to the decision boundary.
D. The centroids of the classes.

32 Which technique allows SVM to perform non-linear classification?

A. Gradient Descent
B. Pruning
C. The Kernel Trick
D. Bagging

33 In SVC (Support Vector Classifier), what does a high value of Gamma () imply for an RBF kernel?

A. The model fits the training data very closely (potential overfitting).
B. Each training example has a wide-reaching influence.
C. The margin becomes wider.
D. The decision boundary will be nearly linear.

34 Which scikit-learn class is used for Support Vector Classification?

A. sklearn.svm.SVR
B. sklearn.tree.DecisionTreeClassifier
C. sklearn.svm.SVC
D. sklearn.linear_model.SGDClassifier

35 The Naïve Bayes classifier is based on which statistical theorem?

A. Pythagorean Theorem
B. Gauss-Markov Theorem
C. Central Limit Theorem
D. Bayes' Theorem

36 What is the "Naïve" assumption in Naïve Bayes?

A. All features are mutually independent given the class.
B. All features are equally important.
C. The data follows a normal distribution.
D. The classes are balanced.

37 Which variant of Naïve Bayes is best suited for continuous data assuming a bell-curve distribution?

A. BernoulliNB
B. GaussianNB
C. MultinomialNB
D. ComplementNB

38 In Text Classification with word counts, which Naïve Bayes variant is typically used?

A. MultinomialNB
B. LinearNB
C. LogisticNB
D. GaussianNB

39 What is Laplace Smoothing used for in Naïve Bayes?

A. To prevent zero probabilities for unseen features.
B. To normalize the dataset.
C. To handle continuous variables.
D. To reduce the number of features.

40 Which of the following classifiers is a Generative Model?

A. Decision Tree
B. Support Vector Machine
C. Naïve Bayes
D. Logistic Regression

41 To handle a multi-class classification problem with a binary classifier like Logistic Regression, which strategy is commonly used?

A. Kernel Trick
B. Gradient Boosting
C. Pruning
D. One-vs-Rest (OvR)

42 Which metric is calculated using the formula: ?

A. Matthews Correlation Coefficient
B. F1-Score
C. Accuracy
D. Specificity

43 If a Decision Tree is fully grown until all leaves are pure, it is likely to have:

A. High Variance (Overfitting)
B. High Bias
C. Low Variance
D. Low Accuracy on training data

44 In the context of the Confusion Matrix, Specificity is also known as:

A. True Negative Rate
B. False Positive Rate
C. True Positive Rate
D. Precision

45 Which scikit-learn utility is best used to split data into training and testing sets?

A. StandardScaler
B. cross_val_score
C. GridSearchCV
D. train_test_split

46 What happens to the decision boundary of a Logistic Regression model if the regularization parameter is very small?

A. The model underfits (high bias).
B. The boundary becomes non-linear.
C. The model overfits.
D. The coefficients become large.

47 Which of the following algorithms does NOT produce a linear decision boundary (without kernels)?

A. Logistic Regression
B. Linear Perceptron
C. Linear SVM
D. k-Nearest Neighbors

48 In SVM, which kernel is defined as ?

A. RBF Kernel
B. Linear Kernel
C. Sigmoid Kernel
D. Polynomial Kernel

49 What is the primary advantage of Naïve Bayes classifiers regarding training time?

A. They are fast because they require a single pass over the data.
B. They are very slow due to iterative optimization.
C. They depend on the number of support vectors.
D. They are slow because they calculate distances between all points.
Previous MCQ
Unit 1
Next MCQ
Unit 3