Machine Learning Classifiers Quiz
Machine Learning Classifiers Quiz
Test your knowledge of machine learning classifiers, algorithms, and evaluation methods with our comprehensive quiz! Whether you're a student, teacher, or just passionate about data science, this quiz will challenge your understanding of key concepts in machine learning.
Key Features:
- 18 thought-provoking multiple choice questions
- Covers a wide range of topics including clustering, boosting, and model evaluation
- Ideal for anyone looking to deepen their knowledge in artificial intelligence
Which of the following is a method of experimentally evaluating the effectiveness of classifiers:
Stratified cross validation
Specificity
Random boosting
Hold-out
Random Forest:
Generates models that have the same weight
Uses return draws to create datasets for which models are generated
Uses a random subset of attributes from which the next decision node is determined
Generates a sequence of models better and better adapted to "difficult" examples
In the dataset (ExampleSet in RapidMiner), an attribute with role:
"Outlier" is required to run an anomaly detection algorithm such as LOF (Local Outlier Factor)
"Regular" requires discretization before running the clustering algorithm
"Label" is required to evaluate the quality of the classifier on the test set
"Label" is required to build a decision tree model
Indicate the true sentences related to eXplainable Artificial Inteligence (XAI).
Explainable artificial intelligence is needed because scientists do not understand how advanced machine learning methods work.
Explainable artificial intelligence methods are those machine learning models that are interpretable.
Methods of explainable artificial intelligence can be independent of the machine learning model used.
The decision tree is not an interpretable model, so it is necessary to apply methods of explainable artificial intelligence to understand on what basis the decision is determined by it
Hierarchical agglomerative clustering algorithm:
Minimises the average distance of objects from group centres
Determines the division into groups, starting with one group containing all objects, which is subdivided until each object belongs to a separate group
Derives a dendrogram illustrating the clustering process
Can correctly identify interlocking concave groups
The effect of the k-means clustering algorithm is:
Fuzzy partition matrix
Dendrogram
Matrix of cluster prototypes
Partitioning into clusters and a collection of objects defined as noise
Boosting:
Generates models that have the same weight
Generates a sequence of models better and better adapted to “difficult” examples
Uses a meta-model to combine the decisions of the ensemble models
Generates a homogeneous ensemble of models
Stacking:
Uses a meta-model to combine the decisions of the ensemble models
Generates a sequence of models where the next model fits to the error made by the previous model
Generates a sequence of models better and better adapted to “difficult” examples
Generates a homogeneous ensemble of models
The effect of the DBSCAN clustering algorithm:
Matrix of cluster prototypes
Fuzzy partition matrix
Dendrogram
Partitioning into clusters and a collection of objects defined as noise
Bagging:
Generates models that have the same weight
Uses return draws to create datasets for which models are generated
Works well for models that strongly fit the data (overfitting)
Generates a sequence of models better and better adapted to “difficult” examples
The effect of the hierarchical agglomerative clustering algorithm is:
Fuzzy partition matrix
Dendrogram
Matrix of cluster prototypes
Partitioning into clusters and a collection of objects defined as noise
DBSCAN clustering algorithm:
Maximises the average distance of objects from group centres
Should be executed several times for different initial conditions, as it may get “stuck” in a local minimum
Derives a dendrogram illustrating the clustering process
can correctly identify interlocking concave groups
K-means clustering algorithm:
Maximises the average distance of objects from group centres
Should be executed several times for different initial conditions, as it may get “stuck” in a local minimum
Requires specifying the number of groups to be identified
can correctly identify interlocking concave groups
Please indicate the true sentences regarding the quality assessment of classifiers
Leave-one-out is a measure of classifiers quality
Cross-validation is a method of experimentally evaluating the performance of classifiers
Accuracy is a measure of quality of classifiers
AUC is a method of experimental evaluation of classifier performance
Tick correct answers
Noninterpretable fuzzy rule base deteriorates the generalization ability of the fuzzy system
The curse of dimensionality is a disadvantage of the grid partition of the input domain
We join descriptors in premises with a T-norm
The grid partition is the most common technique for the input domain partition
Please indicate the true sentences about automatic learning
AML uses hyperparameter optimalization to generate models
AML uses meta-learning to determine the features that will best represent the data that is being analyzed
AML involves generating a predictive model without the need to indicate a value of the decision attribute
AML uses meta-learning to select the best ml methods for the data being analyzed
Evaluate whether it is true or false (Gausser-Müller kernel regression function estimator)
It is possible that estimator value could be interminate
The smoothing parameter value does not influence the final result significantly
Weighted average may be considered as the method interpretation (kernel function values are weights)
The method estimates values only for such arguments that are members of the training set
Tick correct sentences
In neuro-fuzzy systems we can only use differentiable descriptors
It is possible to use a fuzzy set with an infinite support and an empty core in (neuro-) fuzzy systems
It is possible to model relations "greater than", "less than", eg. The temperature is less than...
The centre of gravity method is used for fuzzification
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