Scikit-learn MCQs and Answers With Explanation: Scikit-learn is a highly popular Python library used for data analysis and modeling in the field of Machine Learning. Due to its extensive coverage of algorithms and concepts, it is vital to assess one’s understanding regularly. To aid this, we have compiled a quiz consisting of the Top 30 Scikit-learn MCQs and Answers covering topics such as data preprocessing, model selection, and evaluation metrics.
Scikit-learn MCQs and Answers
Whether you are a beginner or an experienced practitioner, this Scikit-learn Quiz is an excellent way to evaluate your knowledge and enhance your understanding of this powerful library. Let’s delve into the world of Scikit-learn MCQs and Answers and take the first step towards mastering this exciting field.
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Scikit-learn Multiple Choice Questions and Answers
| Quiz Name | Scikit-learn |
| Exam Type | MCQ (Multiple Choice Questions) |
| Category | Technical Quiz |
| Mode of Quiz | Online |
Top 30 Scikit-learn MCQs
1. What is Scikit-learn?
A) A machine learning library in Python
B) A data visualization library in Python
C) A natural language processing library in Python
D) A web development framework in Python
Ans: A
Explanation: Scikit-learn is an open-source machine learning library in Python that provides a range of tools for supervised and unsupervised learning tasks, including classification, regression, clustering, and dimensionality reduction, among others.
2. What is the purpose of the fit() method in Scikit-learn?
A) To train a model using a given dataset
B) To make predictions using a trained model
C) To evaluate the performance of a model
D) To visualize the data using a plot
Ans: A
Explanation: The fit() method is used to train a model using a given dataset. It fits the model parameters to the data, adjusting them to minimize the error between the predicted output and the actual output.
3. Which of the following is an example of a supervised learning algorithm?
A) K-means clustering
B) Decision tree
C) Principal component analysis (PCA)
D) Apriori algorithm
Ans: B
Explanation: Decision tree is an example of a supervised learning algorithm, where the model is trained on labeled data to make predictions on new, unseen data.
4. Which of the following is NOT a classification metric used in Scikit-learn?
A) Precision
B) Recall
C) F1-score
D) R-squared
Ans: D
Explanation: R-squared is a regression metric used to measure the goodness of fit of a model, while the other options are classification metrics used to evaluate the performance of a classification model.
5. Which of the following is a clustering algorithm in Scikit-learn?
A) Random forest
B) K-means
C) Support vector machines (SVM)
D) Gradient boosting
Ans: B
Explanation: K-means is a clustering algorithm in Scikit-learn that groups similar data points together based on their distance from the cluster centroids.
6. Which of the following is an example of a dimensionality reduction algorithm in Scikit-learn?
A) Linear regression
B) K-nearest neighbors (KNN)
C) Principal component analysis (PCA)
D) Naive Bayes
Ans: C
Explanation: PCA is a dimensionality reduction algorithm that transforms high-dimensional data into a lower-dimensional representation while preserving as much of the original variance as possible.
7. What is the purpose of the predict() method in Scikit-learn?
A) To train a model using a given dataset
B) To make predictions using a trained model
C) To evaluate the performance of a model
D) To visualize the data using a plot
Ans: B
Explanation: The predict() method is used to make predictions on new, unseen data using a trained model.
8. Which of the following is NOT a preprocessing step in Scikit-learn?
A) Scaling
B) Imputation
C) Encoding
D) Regularization
Ans: D
Explanation: Regularization is a model parameter tuning technique used to prevent overfitting in machine learning models, while the other options are preprocessing steps used to prepare the data for modeling.
9. Which of the following is an ensemble learning algorithm in Scikit-learn?
A) K-means clustering
B) Decision tree
C) Random forest
D) Linear regression
Ans: C
Explanation: Random forest is an ensemble
learning algorithm in Scikit-learn that combines multiple decision trees to improve the accuracy and robustness of the model.
10. What is the purpose of the score() method in Scikit-learn?
A) To train a model using a given dataset
B) To make predictions using a trained model
C) To evaluate the performance of a model
D) To visualize the data using a plot
Ans: C
Explanation: The score() method is used to evaluate the performance of a trained model using a given metric, such as accuracy or mean squared error.
11. Which of the following is an example of a regression algorithm in Scikit-learn?
A) K-means clustering
B) Decision tree
C) Linear regression
D) Support vector machines (SVM)
Ans: C
Explanation: Linear regression is an example of a regression algorithm in Scikit-learn, where the model is trained on labeled data to predict a continuous output variable.
12. What is cross-validation in Scikit-learn?
A) A method for evaluating the performance of a model
B) A method for preprocessing the data
C) A method for selecting the best features
D) A method for tuning the hyperparameters of a model
Ans: A
Explanation: Cross-validation is a method for evaluating the performance of a model by splitting the data into multiple folds, training the model on one fold and evaluating it on the remaining folds, and repeating this process for each fold.
13. Which of the following is an example of a deep learning algorithm?
A) K-means clustering
B) Decision tree
C) Convolutional neural network (CNN)
D) Principal component analysis (PCA)
Ans: C
Explanation: Convolutional neural network (CNN) is an example of a deep learning algorithm used for image recognition, natural language processing, and other complex tasks.
14. What is the purpose of the transform() method in Scikit-learn?
A) To train a model using a given dataset
B) To make predictions using a trained model
C) To evaluate the performance of a model
D) To preprocess the data for modeling
Ans: D
Explanation: The transform() method is used to preprocess the data for modeling, such as scaling or encoding the features, before training a model.
15. Which of the following is a metric used for clustering evaluation in Scikit-learn?
A) Precision
B) Recall
C) F1-score
D) Silhouette score
Ans: D
Explanation: Silhouette score is a metric used for clustering evaluation in Scikit-learn, which measures the similarity of data points within a cluster and the dissimilarity between different clusters.
16. Which of the following is an example of a semi-supervised learning algorithm?
A) Decision tree
B) K-means clustering
C) Support vector machines (SVM)
D) Label propagation
Ans: D
Explanation: Label propagation is an example of a semi-supervised learning algorithm that uses a small amount of labeled data and a larger amount of unlabeled data to make predictions on new, unseen data.
17. What is the purpose of the GridSearchCV function in Scikit-learn?
A) To preprocess the data for modeling
B) To evaluate the performance of a model
C) To tune the hyperparameters of a model
D) To select the best features for modeling
Ans: C
Explanation: The GridSearchCV function is used to tune the hyperparameters of a model by trying different combinations of hyperparameters and selecting the best combination based on a given evaluation metric.
18. Which of the following is a method for handling missing data in Scikit-learn?
A) Drop the rows with missing data
B) Replace missing data with the mean or median value
C) Use a machine learning algorithm to impute missing values
D) All of the above
Ans: D
Explanation: Scikit-learn provides different methods for handling missing data, including dropping the rows with missing data, replacing missing data with the mean or median value, or using a machine learning algorithm to impute missing values.
19. Which of the following is an example of an ensemble learning algorithm in Scikit-learn?
A) Decision tree
B) K-means clustering
C) Random forest
D) Logistic regression
Ans: C
Explanation: Random forest is an example of an ensemble learning algorithm in Scikit-learn, which combines multiple decision trees to improve the accuracy and robustness of the model.
20. What is the purpose of the predict_proba() method in Scikit-learn?
A) To train a model using a given dataset
B) To make predictions using a trained model
C) To evaluate the performance of a model
D) To compute the probability estimates of the class labels
Ans: D
Explanation: The predict_proba() method is used to compute the probability estimates of the class labels in a classification model, which can be used for ranking or decision-making purposes.
21. Which of the following is a technique for reducing the dimensionality of the data in Scikit-learn?
A) Principal component analysis (PCA)
B) K-means clustering
C) Decision tree
D) Support vector machines (SVM)
Ans: A
Explanation: Principal component analysis (PCA) is a technique for reducing the dimensionality of the data in Scikit-learn, which transforms the data into a lower-dimensional space while preserving most of its variance.
22. Which of the following is an example of a unsupervised learning algorithm in Scikit-learn?
A) Linear regression
B) K-means clustering
C) Support vector machines (SVM)
D) Random forest
Ans: B
Explanation: K-means clustering is an example of an unsupervised learning algorithm in Scikit-learn, which groups the data into clusters based on their similarity without using labeled data.
23. Which of the following is a method for reducing the overfitting of a model in Scikit-learn?
A) Increasing the complexity of the model
B) Decreasing the regularization parameter
C) Increasing the size of the training data
D) None of the above
Ans: B
Explanation: Decreasing the regularization parameter in Scikit-learn can help reduce the overfitting of a model by decreasing the penalty for high complexity, which can lead to a more generalizable model.
24. What is the purpose of the inverse_transform() method in Scikit-learn?
A) To train a model using a given dataset
B) To make predictions using a trained model
C) To evaluate the performance of a model
D) To transform the predicted values back to their original scale
Ans: D
Explanation: The inverse_transform() method is used to transform the predicted values back to their original scale, such as reversing a scaling or encoding process, to make them more interpretable.
25. Which of the following is an example of a classification algorithm in Scikit-learn?
A) K-means clustering
B) Linear regression
C) Naive Bayes
D) PCA
Ans: C
Explanation: Naive Bayes is an example of a classification algorithm in Scikit-learn, which predicts the class labels of the data based on the probability of each class given the input features.
26. Which of the following is a method for evaluating the performance of a classification model in Scikit-learn?
A) R-squared
B) Mean squared error (MSE)
C) Accuracy
D) Root mean squared error (RMSE)
Ans: C
Explanation: Accuracy is a method for evaluating the performance of a classification model in Scikit-learn, which measures the proportion of correctly predicted instances out of all instances.
27. Which of the following is an example of a kernel function in Scikit-learn?
A) Linear
B) Polynomial
C) Gaussian (RBF)
D) All of the above
Ans: D
Explanation: Scikit-learn provides different kernel functions for support vector machines (SVM), including linear, polynomial, and Gaussian (RBF) kernel, among others.
28. Which of the following is a method for selecting the most important features in a dataset in Scikit-learn?
A) Recursive feature elimination
B) Principal component analysis (PCA)
C) t-SNE
D) None of the above
Ans: A
Explanation: Recursive feature elimination is a method for selecting the most important features in a dataset in Scikit-learn, which iteratively removes the least important features based on a given scoring metric until a desired number of features is reached.
29. Which of the following is an example of a clustering algorithm in Scikit-learn?
A) Decision tree
B) Logistic regression
C) K-means clustering
D) Random forest
Ans: C
Explanation: K-means clustering is an example of a clustering algorithm in Scikit-learn, which groups the data into clusters based on their similarity without using labeled data.
30. Which of the following is a method for selecting the best hyperparameters for a model in Scikit-learn?
A) Grid search
B) Random search
C) Bayesian optimization
D) All of the above
Ans: D
Explanation: Scikit-learn provides different methods for selecting the best hyperparameters for a model, including grid search, random search, and Bayesian optimization, among others. These methods involve trying different combinations of hyperparameters and selecting the best combination based on a given evaluation metric.
The Scikit-learn MCQs and Answers offer an extensive evaluation of individuals’ comprehension of this influential Python library used in Machine Learning. To further enhance your learning, we recommend following freshersnow.com on a daily basis as it provides valuable insights into various fields.