MCQs On Python For Machine Learning With Answers! #28

A quick knowledge check!

Hello Everyone,

Welcome to the 28th edition of my newsletter ML & AI Cupcakes!

The agenda of today’s newsletter is to have a quick check of your understanding of Python for Machine Learning.

I have compiled a practice set which contains questions related to data preprocessing, model parameters, model training, evaluation metrics etc.

Before we start the quiz, just wanted to share something with you.

If you are a complete beginner in the field of machine learning and python, you can refer to the following cheat sheet to kick start your journey.

There is a lot more to learn, but this will help you to atleast start with your first machine learning project. These python commands are very basic and easy to understand.

Let me know if you want me to create more of such cheat sheets for you!

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Multiple Choice Questions (MCQs)

If you don’t know any of the answers or want to understand a topic in more detail, I recommend you take help from scikit-learn documentation .

1. StandardScaler in scikit-learn is used for ____ .

   1. Feature scaling

   2. Feature selection

   3. Imputing missing values

   4. Removing duplicates

2. Which of the following is used to transform the data into a standard normal distribution?

   1. StandardScaler()

   2. MinMaxScaler()

   3. SimpleImputer()

   4. LabelEncoder()

3. Which of the following can be used to handle missing values?

   1. StandardScaler()

   2. MinMaxScaler()

   3. SimpleImputer()

   4. LabelEncoder()

4. What is the use of OneHotEncoder in scikit-learn?

   1. Feature scaling

   2. Feature selection

   3. Feature encoding

   4. Data normalization

5. The default range for MinMaxScaler in scikit-learn is _____ .

   1. (0,1)

   2. (-1,1)

   3. (-1,0)

   4. (0, infinity)

6. Which scikit-learn module is used to split the data into training and test sets?

   1. datasets

   2. preprocessing

   3. model_selection

   4. metrics

7. Which library is used for training machine learning models in Python?

   1. numpy

   2. pandas

   3. sklearn

   4. matplotlib

8. What is the correct syntax for creating a DataFrame in pandas.

   1. pd.selectDataFrame(data, cols =[“A”, “B”])

   2. pd.DataFrame(data, columns =[“A”, “B”])

   3. pd.newDataFrame(data, columns =[“A”, “B”])

   4. pd.createDataFrame(data, columns =[“A”, “B”])

9. What is the use of fit() method in scikit-learn?

   1. Data visualization

   2. Data preprocessing

   3. Model training

   4. Model evaluation

10. Which method is used to make model predictions in scikit-learn?

    1. predictions()

    2. predict()

    3. fit()

    4. transform()

11. What does cross_val_score function in scikit-learn return?

    1. average of scores for all the folds

    2. standard deviation of scores for all the folds

    3. mode of scores for all the folds

    4. a list of scores for each fold

12. What is the default node splitting criterion in DecisionTreeClassifier in scikit-learn?

    1. gini index

    2. entropy

    3. log_loss

    4. None of the above

13. What is the use of GridSearchCV in scikit-learn?

    1. Data preprocessing

    2. Model training

    3. Hyperparameter tuning

    4. Data Visualization

14. Which class in scikit-learn is used for K-Nearest Neighbor (KNN) algorithm?

    1. KNN

    2. KNeighborsClassifier

    3. KNearestNeighbor

    4. KNNClassifier

15. Which class in sklearn.linear_model module implements L1 regularization?

    1. Lasso

    2. Ridge

    3. LinearRegression

    4. ElasticNet

16. Which class in sklearn.linear_model module implements L2 regularization?

    1. Lasso

    2. Ridge

    3. LinearRegression

    4. ElasticNet

17. Is regularization applied by default for LogisticRegression in scikit-learn?

    1. Yes

    2. No

18. Which parameter is used by Logistic Regression in scikit-learn to define regularization type?

    1. penalty

    2. fit_intercept

    3. solver

    4. max_iter

19. Which of the following is not a parameter of Decision Tree Classifier in scikit-learn?

    1. max_depth

    2. criterion

    3. penalty

    4. min_samples_split

20. Which parameter is present in RandomForestClassifier in scikit-learn, but not in DecisionTreeClassifier?

    1. criterion

    2. max_depth

    3. max_features

    4. n_estimators

21. Which function is used to calculate accuracy scikit-learn?

    1. metrics.accuracy

    2. metrics.accuracy_score

    3. metrics.calculate_accuracy

    4. metrics.cross_val_score

22. There is no difference between fit() and fit_transform() methods in scikit_learn.

    1. True

    2. False

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Answers

1. a 2. a 3. c 4. c 5. a 6. c 7. c 8. b 9. c 10. b 11. d 12. a 13. c 14. b 15. a 16.b 17. a 18. a 19. c 20. d 21. b 22. b

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-Kavita