1. Which of the following utility of Pandas can be used to read from Oracle database?
View Answer
read_sql
2. What do the methods starting with fetch, of sklearn.datasets module do?
View Answer
It downloads a specific dataset from a library.
3. Which of the following function is used for loading famous iris dataset from sklearn.datasets?
View Answer
load_iris()
4. Which of the following Python library is used for Machine Learning?
View Answer
Scikit-Learn
5. Which of the following module of sklearn contains popular datasets, which are processed?
View Answer
datasets
6. What is the type of iris variable, shown in the below expression?
View Answer
from sklearn import datasets
iris = datasets.load_iris()
sklearn.datasets.base.Bunch
7. What value does the attribute DESCR of a specific loaded dataset contain?
View Answer
Contains a description of the loaded dataset
8. scikit-learn provides utilities for building artificial datasets.
View Answer
True
9. Which of the following library is widely used to read data from external sources with structured data?
View Answer
Pandas
10. Which of the following expressions can access the features of the iris dataset, shown in the below expression?
View Answer
from sklearn import datasets
iris = datasets.load_iris()
iris.data(doubt)
11. Scikit-learn provides Pipeline utility to build a pipeline, which performs a series of transformations.
View Answer
True
12. The preprocessing technique in which categorical values are transformed to categorical integers is known as ________________.
View Answer
Encoding
13. The preprocessing technique in which missing values are replaced with the mean of a dataset is known as _______________.
View Answer
Imputing
14. The preprocessing technique in which a dataset is transformed to a distribution of mean 0 and variance 1 is known as __________________.
View Answer
Mean removal
15. Which of the following API is used to normalize a sample to the unit norm?
View Answer
Normalize
16. Which of the following API is used to scale a dataset to range 0 and 1?
View Answer
MinMaxScaler
17. import sklearn.preprocessing as preprocessing
regions = ['HYD', 'CHN', 'MUM', 'HYD', 'KOL', 'CHN']
print(preprocessing.LabelEncoder().fit(regions).transform(regions))
View Answer
[1 0 3 1 2 0]
18. Which of the following module of sklearn contains preprocessing utilities?
View Answer
Preprocessing
19. ________ parameter is used to control the number of neighbors of KNearestClassifier.
View Answer
n_neighbors
20. Which regressor utility of sklearn.neighbors is used to learn from k nearest neighbors of each query point?
View Answer
KNeighborsRegressor
21. Which of the following parameter can be used to give more weightage to the points, which are nearer to a point in the nearest neighbors method?
View Answer
weights
22. Which of the following class is used to implement the K-Nearest Neighbors classification in scikit-learn?
View Answer
KNeighborsClassifier
23. Which of the following algorithms can be used with any nearest neighbors utility in scikit-learn?
View Answer
all
24. Which of the following is an essential parameter of RadiusNeighborsClassifier?
View Answer
radius
25. Neighbors-based regression is mainly used when the data labels are continuous rather than discrete variables.
View Answer
True
26. What is the strategy followed by Radius Neighbors method?
View Answer
It looks in the vincinity of area, covered by a fixed radius, of each training point.
27. Which of the following module of sklearn is used to deal with Nearest Neighbors?
View Answer
neighbors
28. A feature can be reused to split a tree during Decision tree creation.
View Answer
True
29. Which of the following parameter is used to tune a Decision Tree?
View Answer
max_depth
30. Which of the following module of sklearn is used for dealing with Decision Trees?
View Answer
tree
31. A small change in data features may change a Decision Tree completely.
View Answer
True
32. Which of the following utility is used for regression using decision trees?
View Answer
DecisionTreeRegressor
33. Decision trees overfit the data very easily.
View Answer
True
34. Ensemble methods are better than Decision Trees.
View Answer
True
35. More improvement is found in an ensemble when base estimators are highly correlated?
View Answer
False
36. Which parameter is used to manage many base estimators in RandomForestClassifier?
View Answer
n_estimators
37. Which of the following module of sklearn is used for dealing with ensemble methods?
View Answer
ensemble
38. Which of the following utility of sklearn.ensemble is used for implementing classification with the bagging method?
View Answer
BaggingClassifier
39. Which of the following utilities are provided by sklearn to perform classification using support vector machines?
View Answer
All the options
40. What values can be used for kernel parameter of SVC class?
View Answer
All the options
41. Scaling or Normalization of data improves the accuracy of support vector machines.
View Answer
True
42. LinearSVC class accepts kernel parameter value.
View Answer
False
43. Which attribute provides details of obtained support vectors, after classifying data using SVC?
View Answer
support_vectors_
44. Which approach is used by SVC and NuSVC for multi-class classification?
View Answer
one vs one
45. What happens when very small value is used for parameter C in support vector machines?
View Answer
None
46. Which of the following parameter of SVC method is used for fine-tuning the model?
View Answer
C
47. Which of the following module of sklearn provides the utilities to deal with support vector machines?
View Answer
svm
48. Which of the following utility of sklearn.cluster is used for performing k-means clustering?
View Answer
KMeans()
49. Agglomerative Clustering follows a top-down approach.
View Answer
False
50. Which of the following parameters are used to control Density-based clustering?
View Answer
eps, min_samples
51. What does the Homogeneity score of a clsutering algorithm indicate ?
View Answer
Verifies if each cluster contains only members of a single class.
52. Which of the following clustering technique is used to group data points into user given k clusters?
View Answer
K-means clustering
53. Spectral Clustering is best suited for identifying dense clusters.
View Answer
False
54. What values can be used for the linkage parameter in AgglomerativeClustering
View Answer
All
55. Ensemble learns to combine weak learners to become a strong learner.
View Answer
True
56. Data used for Decision Trees have to be preprocessed compulsorily.
View Answer
False
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