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- Multiple ChoicePlease save your changes before editing any questions.
Traditional machine learning and deep learning are the core technologies of artificial
intelligence. There is a slight difference in the engineering process. The following steps. What
you don't need to do in deep learning is:
Model evaluation
Feature engineering
Data cleaning
Model building
- Multiple ChoicePlease save your changes before editing any questions.
Which of the following description of the validation set is wrong?
The verification set can coincide with the test set.
The test set can coincide with the training set
The subset used to pick hyperparameters is called a validation set.
Typically 80% of the training data 1s used for training and 20% 1s used for verification.
- Multiple ChoicePlease save your changes before editing any questions.
What are the common clustering algorithms?
Density clustering
Hierarchical clustering
Spectral clustering
k-means
All the above
- Multiple ChoicePlease save your changes before editing any questions.
The model composed of machine learning algorithms cannot represent the true data
distribution function on a theoretical level. Just approach it.
TRUE
FALSE
- Multiple ChoicePlease save your changes before editing any questions.
What is the performance of artificial intelligence in the stage of perceptual intelligence?
Machines begin to understand, think and make decisions like humans.
Machines begin to calculate and transmit information just like humans.
The machine starts to understand and understand, make judgments, and take some simple actions
- Multiple ChoicePlease save your changes before editing any questions.
Which of the following is true about unsupervised learning?
Unsupervised algorithm only processes "features" and does tags.
Dimensionality reduction algorithm is not unsupervised learning
K-means algorithm and SVM algorithm belong lo unsupervised learning.
none of the above.
- Multiple ChoicePlease save your changes before editing any questions.
The training error will continue to decrease as the model complexity increases.
TRUE
FALSE
- Multiple ChoicePlease save your changes before editing any questions.
Which of the following statements about supervised learning is correct?
Decision tree is a supervised learning
Supervised learning cannot use cross-validation for training.
Supervised learning is a rule-based algorithm
Supervised learning can be trained without labels
- Multiple ChoicePlease save your changes before editing any questions.
Jobs that are repetitive and require weak social skills are the easiest to be AI Replaced work.
TRUE
FALSE
- Multiple ChoicePlease save your changes before editing any questions.
Reducing the gap between the training error and the test error will result in over-fitting How to
prevent over-fitting?
Cross validation
Integration method
Increase regularization
Feature Engineering
All the above.
- Multiple ChoicePlease save your changes before editing any questions.
The test error will keep getting smaller as the complexity of the model increases.
TRUE
FALSE
- Multiple ChoicePlease save your changes before editing any questions.
After the data has completed the feature engineering operation, in the process of
constructing the model, which of the following options is not a step in the decision tree construction process?
Pruning
Feature selection
Data cleaning
Decision tree generation
- Multiple ChoicePlease save your changes before editing any questions.
What does factors that promote the development of artificial intelligence not include?
Big data
Computing ability
Algorithm theory
Block chain
- Multiple ChoicePlease save your changes before editing any questions.
In machine learning, what input the model needs to train itself and predict the unknown?
Manual procedure
Neural Networks
Training algorithm
historical data
- Multiple ChoicePlease save your changes before editing any questions.
What does not belong to supervised learning?
Logistic regression
Support vector machine
Decision tree
Princi1pal component analysis
- Multiple ChoicePlease save your changes before editing any questions.
Training error will reduce the accuracy of the model and produce under-fitting. How to improve the model fit?
a. Increase the amount of data
b.Feature Engineering
c.Reduce regularization parameters
d.Add features
ABD
- Multiple ChoicePlease save your changes before editing any questions.
.Among machine learning algorithms, which of the following is not unsupervised
learning?
GMM
Xgboost
Clustering
Association rules
- Multiple ChoicePlease save your changes before editing any questions.
Which description is wrong about the hyperparameter?
hyperparameters are parameters that set values before the algorithm begins learning.
Most machine learning algorithms have hyperparameters.
Hyperparameters cannot be modified
The value of the hyperparameter is not learned by the algorithm itself.