AI with Orange Data Mining Tool Class 12 MCQ, Data mining is the process of discovering trends, useful information, and patterns from large datasets. It involves analyzing and interpreting data to extract meaningful insights that can aid in decision-making.
AI with Orange Data Mining Tool Class 12 MCQ
1. Which widget helps in knowing the Accuracy, Precision, recall and F1 score values?
a. Confusion matrix
b. Test and score
c. Word cloud
d. Corpus
2. Which widget provides the detailed breakdown of the model’s classifications, showing the number of true positives, true negatives, false positives, and false negatives for each class?
a. Test and score
b. Preprocess text
c. Confusion Matrix
d. Corpus
3. __ widget performs text normalization by converting text to lowercase, tokenizing it into individual words, removing punctuation, and filtering out stop words.
Show Answer ⟶4. Cross validation can also be called as _
a. Rotation estimation
b. Cross hybrid
c. Revolution Estimation
d. Estimation
5. In word cloud, more the frequency of word in corpus larger the size of word would look.
a. True
b. False
6. Which widget is used to transform the raw images into numerical representations?
a. Image viewer
b. Image analytics
c. Image Embedding
d. Distance
7. Which widget is used to compare the embeddings of images and compute their similarities?
a. Distance
b. Image Embedding
c. Preprocess Text
d. Image viewer
8. __ are lines that link widgets together on the canvas. They represent the flow of data from one widget to another, indicating how the output of one widget is used as input for another.
Show Answer ⟶9. Suppose you are working as a data scientist for a social media analytics company. The company is interested in analyzing and categorizing images shared on its platform to better understand user behavior and preferences. Which widget will you use to extract numerical representations of images for analysis?
Show Answer ⟶10. Primary way to collect DATA (Data Gathering process)?
a. Experiment
b. Survey
c. Interview
d. Observation
11. Which of the following options is/are true for K-fold cross-validation?
1) Increase in K will result in higher time required to cross validate the result.
2) Higher values of K will result in higher confidence on the cross-validation result as compared to lower value of K.
3) If K=N, then it is called Leave one out cross validation, where N is the number of observations
a. 1 and 2
b. 2 and 3
c. 1 and 3
d. 1, 2 and 3
12. Which of the following statements is/are INCORRECT
i) The volume of test data can be large, which presents complexities.
ii) Your testing team should test the AI and ML algorithms keeping model validation, successful learnability, and algorithm effectiveness in mind.
iii) Test data should include all irrelevant subsets of training data, i.e., the data you will use for training the AI system.
a. None of the Below
b. ii)
c. iii)
d. i),ii) & iii)
13. Which of the following statements is/are INCORRECT:
i) Different transforms of the data used to train the same machine learning model.
ii) Different machine learning models cannot be trained on the same data.
iii) Different configurations for a machine learning model trained on the same data
a. i)
b. ii)
c. Both ii) & iii)
d. Both i) & ii)
14. The train-test procedure is appropriate when there is a sufficiently _ data sets available.
a. Comparative
b. Large
c. Small
d. Equal
15. Which of the following is NOT true for Train-Test Split Evaluation ?
a. The procedure involves taking a dataset and dividing it into two subsets.
b. The train-test procedure is appropriate when there is a larger dataset.
c. The objective is to estimate the performance of the user.
d. It can be used for classification or regression problems.
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