If there is only a discrete number of possible outcomes called          .

Related MCQs

• if there is only a discrete number of possible outcomes (called categories), the process becomes a           .
• if there is only a discrete number of possible outcomes (called categories), the process becomes a            .
• if there is only a discrete number of possible outcomes (called categories), the process becomes a .
• if there is only a discrete number of possible outcomes (called categories), the process becomes a______.
• If there is only a discrete number of possible outcomes called _____.
• A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?
• The selling price of a house depends on many factors. For example, it depends on the number of bedrooms, number of kitchen, number of bathrooms, the year the house was built, and the square footage of the lot. Given these factors, predicting the selling price of the house is an example of ____________ task.
• Suppose we train a hard-margin linear SVM on n > 100 data points in R2, yielding a hyperplane with exactly 2 support vectors. If we add one more data point and retrain the classifier, what is the maximum possible number of support vectors for the new hyperplane (assuming the n + 1 points are linearly separable)?
• Which of the following can act as possible termination conditions in K-Means? 1. For a fixed number of iterations. 2. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum. 3. Centroids do not change between successive iterations. 4. Terminate when RSS falls below a threshold.
• Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11. What were the original responses for data points along the two branches (left & right respectively) and what is the new response after collapsing the node?