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Python Multiple Choice Questions for Beginners PDF
Which Matplotlib function is used to generate a scatter plot with a regression line?
a. plt.scatter()
b. plt.plot()
c. plt.regplot()
d. plt.lineplot()
Option c – plt.regplot()
What represents the general formula for a basic linear regression model?
a. y = mx + c
b. y = mx
c. y = ax² + bx + c
d. y = ab^x
Option a – y = mx + c
In scikit-learn, which method is used to generate predictions after training a linear regression model?
a. fit()
b. transform()
c. predict()
d. score()
Option c – predict()
In the context of linear regression, what are residuals?
a. The gap between predicted and actual output values
b. Parameters in the regression formula
c. The constant term in the regression equation
d. Variation in the dataset
Option a – The gap between predicted and actual output values
Which Python package is frequently used to detect multicollinearity issues in linear regression?
a. NumPy
b. Pandas
c. Statsmodels
d. seaborn
Option c – Statsmodels
In multiple linear regression, what does the Variance Inflation Factor (VIF) indicate?
a. The degree of relationship between each independent variable and the target
b. The influence of outliers on the regression
c. The level of collinearity among the independent variables
d. The model’s predictive capability
Option c – The level of collinearity among the independent variables
Which test is commonly applied to check the statistical relevance of coefficients in linear regression?
a. T-test
b. ANOVA
c. Chi-square test
d. F-test
Option a – T-test
What role does regularization play in linear regression?
a. It helps to lower bias in the model
b. It increases the number of input variables
c. It leads to overfitting
d. It boosts model variance
Option a – It helps to lower bias in the model
Which function in scikit-learn is used to perform Ridge regression?
a. Ridge()
b. LinearRegression()
c. Lasso()
d. fit()
Option a – Ridge()
What regularization methods can be used to reduce overfitting by penalizing large weights?
a. L1 Regularization (Lasso)
b. L2 Regularization (Ridge)
c. Elastic Net
d. Both a and b
Option d – Both a and b
Why is feature scaling important in linear regression?
a. To bring all independent variables within a similar scale
b. To add more input features
c. To eliminate anomalies in data
d. To lower processing time
Option a – To bring all independent variables within a similar scale
Which metric evaluates model fit in linear regression while penalizing extra features?
a. AIC (Akaike Information Criterion)
b. BIC (Bayesian Information Criterion)
c. RMSE (Root Mean Squared Error)
d. MSE (Mean Squared Error)
Option b – BIC (Bayesian Information Criterion)
In linear regression, what does heteroscedasticity describe?
a. Irregularity in data relationships
b. Differences in residual spread at different values of an independent variable
c. Strong correlation among predictor variables
d. The effect of extreme values in data
Option b – Differences in residual spread at different values of an independent variable
Which function in scikit-learn is commonly used to carry out Decision Tree-based regression for modeling nonlinear data?
a. DecisionTreeRegressor()
b. SVR()
c. MLPRegressor()
d. RandomForestRegressor()
Option a – DecisionTreeRegressor()
How does linear regression differ from nonlinear regression?
a. Linear regression assumes a straight-line relationship, whereas nonlinear regression supports more complex patterns
b. Linear regression consumes more processing power than nonlinear regression
c. Linear regression needs feature normalization, but nonlinear regression doesn’t
d. Linear regression always produces more accurate predictions than nonlinear models
Option a – Linear regression assumes a straight-line relationship, whereas nonlinear regression supports more complex patterns
What technique in scikit-learn helps tackle nonlinear regression with large datasets by breaking the data into multiple parts?
a. Bagging
b. Boosting
c. Random Forests
d. Gradient Boosting
Option a – Bagging
Which scikit-learn class is mainly used for applying Random Forest Regression to nonlinear problems?
a. DecisionTreeRegressor()
b. SVR()
c. RandomForestRegressor()
d. MLPRegressor()
Option c – RandomForestRegressor()
Which kind of nonlinear regression curve approaches a flat limit as the input increases?
a. Exponential function
b. Sigmoid curve
c. Polynomial function
d. Power law equation
Option b – Sigmoid curve
In nonlinear regression, what does the phrase “local minima” refer to?
a. The point where the regression curve intersects the vertical axis
b. The lowest location on the regression line
c. Several curves with individual minimum points
d. A spot where the slope of the error function becomes zero
Option d – A spot where the slope of the error function becomes zero
What strategy is useful in nonlinear regression to reduce the impact of multi-collinearity among input variables?
a. Feature scaling
b. Applying regularization techniques
c. Using Principal Component Analysis (PCA)
d. Polynomial feature expansion
Option c – Using Principal Component Analysis (PCA)
Which Python package includes the tools needed to run the K-Nearest Neighbors (KNN) algorithm?
a. NumPy
b. scikit-learn
c. TensorFlow
d. Pandas
Option b – scikit-learn
The K-Nearest Neighbors algorithm falls under which category of machine learning?
a. Unsupervised learning
b. Supervised learning
c. Semi-supervised learning
d. Reinforcement learning
Option b – Supervised learning
In the context of the KNN algorithm, what does the letter “K” indicate?
a. The number of groups or clusters
b. The number of input variables
c. The count of closest neighboring data points considered
d. The number of times the algorithm runs
Option c – The count of closest neighboring data points considered
Which scikit-learn method is used to build or fit a KNN model to the data?
a. fit()
b. predict()
c. transform()
d. score()
Option a – fit()
What core idea underlies the K-Nearest Neighbors algorithm?
a. Data must be separable by a linear boundary
b. Input values should follow a normal distribution
c. Nearby instances are expected to share similar labels or output values
d. The dataset must have a uniform distribution across classes
Option c – Nearby instances are expected to share similar labels or output values
In the K-Nearest Neighbors algorithm, which parameter determines the method used to identify the nearest points?
a. n_neighbors
b. weights
c. algorithm
d. metric
Option c – algorithm
Which technique can be used in the KNN algorithm to handle missing data?
a. Imputation
b. Deletion
c. Ignoring missing values
d. All of the above
Option a – Imputation
Which Python package provides built-in tools for working with decision tree models?
a. NumPy
b. TensorFlow
c. scikit-learn
d. Pandas
Option c – scikit-learn
Decision tree models fall under which category of machine learning?
a. Unsupervised learning
b. Reinforcement learning
c. Supervised learning
d. Semi-supervised learning
Option c – Supervised learning
What is the primary aim of a decision tree model?
a. To maximize information gain
b. To minimize the number of decision nodes
c. To simplify feature selection
d. None of the above
Option a – To maximize information gain
Which function in scikit-learn is used to train a decision tree model?
a. fit()
b. predict()
c. transform()
d. score()
Option a – fit()
What is commonly used to determine node splits in a decision tree?
a. Gini impurity
b. Information gain
c. Entropy
d. All of the above
Option d – All of the above
Which argument in a decision tree configuration controls how deep the tree can go?
a. max_depth
b. min_samples_split
c. criterion
d. splitter
Option a – max_depth
What does a terminal or leaf node in a decision tree signify?
a. A decision rule
b. A data feature
c. A predicted class or value
d. A split condition
Option c – A predicted class or value
Which scikit-learn method is used for making predictions after a decision tree has been trained?
a. fit()
b. transform()
c. predict()
d. score()
Option c – predict()
In the context of decision trees, what does pruning involve?
a. Adding new branches to the tree
b. Removing certain nodes to reduce complexity and prevent overfitting
c. Reducing the overall depth of the model
d. None of the above
Option b – Removing certain nodes to reduce complexity and prevent overfitting
What strategy is commonly used in decision trees to handle missing feature values?
a. Dropping rows that contain missing values
b. Replacing with the most common value of that feature
c. Filling with random values
d. None of the above
Option b – Replacing with the most common value of that feature
How does increasing the depth of a decision tree impact the balance between bias and variance?
a. Bias goes up and variance goes down
b. Bias goes down and variance goes up
c. Both bias and variance increase
d. Both bias and variance decrease
Option c – Both bias and variance increase
Which decision tree parameter controls the least number of samples required to divide a node?
a. max_depth
b. min_samples_split
c. criterion
d. splitter
Option b – min_samples_split
Which evaluation metric is frequently applied to measure the effectiveness of a logistic regression model?
a. R-squared
b. Accuracy
c. Mean Squared Error (MSE)
d. F1-score
Option b – Accuracy
What function does the sigmoid curve serve in logistic regression?
a. Scales the input data
b. Converts real-valued inputs into probabilities between 0 and 1
c. Reduces the dataset’s dimensionality
d. Computes error values
Option b – Converts real-valued inputs into probabilities between 0 and 1
Which scikit-learn method is used to generate predictions from a fitted logistic regression model?
a. fit()
b. transform()
c. predict()
d. score()
Option c – predict()
What loss function is commonly used for optimizing logistic regression models?
a. Mean Squared Error (MSE)
b. Cross-Entropy Loss
c. R-squared
d. Absolute Error
Option b – Cross-Entropy Loss
Which parameter in logistic regression helps in avoiding overfitting by penalizing overly large coefficients?
a. C
b. penalty
c. solver
d. max_iter
Option b – penalty
Which function in scikit-learn adjusts the regularization intensity for logistic regression?
a. set_penalty()
b. set_C()
c. set_regularization()
d. set_strength()
Option b – set_C()
What does the term logit refer to in the context of logistic regression?
a. Likelihood of a positive classification
b. Linear combination of input variables and their weights
c. Likelihood of a negative classification
d. None of the above
Option b – Linear combination of input variables and their weights
Which logistic regression parameter selects the optimization algorithm used during training?
a. C
b. penalty
c. solver
d. max_iter
Option c – solver
What is a common approach logistic regression uses to manage multicollinearity among features?
a. Automatically eliminates repetitive variables
b. Assigns higher weights to highly correlated inputs
c. Disregards collinear features during training
d. None of the above
Option d – None of the above
Which method from scikit-learn is typically used to evaluate the prediction accuracy of a logistic regression model?
a. evaluate()
b. accuracy_score()
c. score()
d. predict()
Option c – score()
In logistic regression, what does the odds ratio indicate?
a. The likelihood of an event taking place
b. The proportion between success probability and failure probability
c. The deviation between expected and actual outputs
d. None of the above
Option b – The proportion between success probability and failure probability
Which parameter in logistic regression determines the maximum number of optimization iterations?
a. C
b. penalty
c. solver
d. max_iter
Option d – max_iter
Which scikit-learn attribute provides access to the weight coefficients of a logistic regression model?
a. get_coefficients()
b. coef_()
c. retrieve_parameters()
d. weights_0
Option b – coef_()
Which SVM parameter affects how smooth or complex the decision boundary is?
a. gamma
b. C
c. kernel
d. degree
Option a – gamma
What impact does a higher value of C have on an SVM model?
a. It strengthens the regularization effect
b. It makes the margin wider
c. It narrows the margin
d. None of the above
Option a – It strengthens the regularization effect
Which SVM kernel functions similarly to a neural network using a sigmoid activation?
a. Linear kernel
b. Polynomial kernel
c. RBF (Radial Basis Function) kernel
d. Sigmoid kernel
Option d – Sigmoid kernel
In scikit-learn, which function allows you to specify the kernel used in an SVM model?
a. set_kernel()
b. kernel_type()
c. set_params()
d. set_kernel_type()
Option c – set_params()
What strategy does SVM use to perform multi-class classification?
a. One-vs-One approach
b. One-vs-Rest approach
c. SVM is not capable of handling multiple classes
d. None of the above
Option a – One-vs-One approach
Which parameter in SVM defines the polynomial kernel’s degree?
a. gamma
b. C
c. kernel
d. degree
Option d – degree
How can you access the support vectors from an SVM model in scikit-learn?
a. get_support_vectors()
b. support_vectors_()
c. retrieve_support_vectors()
d. svm_vectors_
Option b – support_vectors_()
What happens to bias and variance in SVM as the value of C increases?
a. Bias rises while variance drops
b. Bias drops while variance rises
c. Both bias and variance go up
d. Both bias and variance go down
Option c – Both bias and variance go up
Which scikit-learn function is used to assess an SVM model’s accuracy?
a. evaluate()
b. accuracy_score()
c. score()
d. predict()
Option c – score()
Which Python package includes tools for calculating evaluation metrics?
a. NumPy
b. TensorFlow
c. scikit-learn
d. Pandas
Option c – scikit-learn
What metric is most appropriate for evaluating classification models on imbalanced datasets?
a. Accuracy
b. Precision
c. Recall
d. F1-score
Option d – F1-score
What is the role of a confusion matrix in assessing a model’s performance?
a. It estimates how complex the model is
b. It provides a visual summary of a classifier’s results
c. It helps determine the model’s prediction capability
d. None of the above
Option b – It provides a visual summary of a classifier’s results
Which scikit-learn method is used to create a confusion matrix?
a. confusion_matrix()
b. generate_confusion_matrix()
c. evaluate_confusion()
d. matrix_evaluation()
Option a – confusion_matrix()
When minimizing false negatives is important, which evaluation metric should be prioritized in imbalanced classification problems?
a. Accuracy
b. Precision
c. Recall
d. F1-score
Option c – Recall
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