Midter Ai fundendametal reviewer 1 Flashcards
Hierarchical clustering is a type of ______________ technique.
Clustering
How are batch learning algorithms typically used?
To predict continuous values in batch mode
How is the line of best fit calculated using the least squares method?
By minimizing the sum of the squares of the errors between the data points and the line of best fit
How can the problem of producing suboptimal results if the clusters are not spherical be addressed in the k-means algorithm?
By using a hierarchical clustering approach
How does supervised learning differ from unsupervised learning?
Supervised learning involves labeled data, while unsupervised learning involves unlabeled data
How does the k-means algorithm determine which data points belong to which cluster?
By computing the distance between data points and the centroid of each cluster
How does the least squares method handle outliers in the data set?
It ignores them
How can the sensitivity to the initial placement of centroids be addressed in the k-means algorithm?
By using the k-means++ initialization method
Can the Naive Bayes classifier handle missing or incomplete data?
Yes, it can handle missing or incomplete data
Hierarchical clustering can be either
Agglomerative, divisive
Hierarchical clustering is a type of ______________ clustering.
Hierarchical
How is KNIME different from other data analysis tools?
t allows users to build custom data pipelines
How can users access the KNIME Marketplace?
All of the above
How does the Naive Bayes classifier calculate the probability of a data point belonging to a particular class?
By using the Bayes theorem
How is the Hebb rule used in the training of a neural network?
It is used to adjust the weights of the neural network based on the input and output
How is the final set of clusters determined in the k-means algorithm?
By selecting the set of clusters that minimize the sum of squared errors
Hierarchical clustering is sensitive to the ______________ of the data.
All of the above
Can the least squares method be used for multiple linear regression?
Yes
How is the Hebb rule different from the delta rule?
The Hebb rule uses the error between the output and target to update the weights, while the delta rule uses the input and output
Can the least squares method be used for nonlinear data sets?
No
