Python for Data Science and Machine Learning
Python for Data Science and Machine Learning - Important Points
| 31. | Which of the following is a technique used for handling missing data in machine learning? |
|---|
A. Imputation
B. Standardization
C. One-hot encoding
D. Gradient descent
View Answer Discuss Work SpaceAnswer: option a
Explanation:
Imputation is a technique used for handling missing data in machine learning, by replacing missing values with estimated values based on the available data.
| 32. | Which of the following is a type of neural network architecture in deep learning? |
|---|
A. Convolutional neural network
B. Random forest
C. K-means clustering
D. None of the above
View Answer Discuss Work SpaceAnswer: option a
Explanation:
Convolutional neural network is a type of neural network architecture used in deep learning, which is commonly used for image and video recognition tasks.
| 33. | Which of the following is a Python library for natural language processing? |
|---|
A. Matplotlib
B. NumPy
C. Pandas
D. NLTK
View Answer Discuss Work SpaceAnswer: option d
Explanation:
NLTK (Natural Language Toolkit) is a Python library for natural language processing, providing a suite of tools and resources for text analysis and language modeling.
| 34. | Which of the following is a method for handling categorical data in machine learning? |
|---|
A. One-hot encoding
B. PCA
C. Standardization
D. Gradient descent
View Answer Discuss Work SpaceAnswer: option a
Explanation:
One-hot encoding is a method used for handling categorical data in machine learning, by converting categorical variables into binary vectors, with each category represented by a binary feature.
| 35. | Which of the following is a type of regularization technique in machine learning? |
|---|
A. Lasso regularization
B. K-means clustering
C. Gradient descent
D. None of the above
View Answer Discuss Work SpaceAnswer: option a
Explanation:
Lasso regularization is a type of regularization technique used in machine learning, which adds a penalty term to the loss function to encourage sparse solutions.