HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:
Suggested Answer:
Box 1: Yes - For regression problems, the label column must contain numeric data that represents the response variable. Ideally the numeric data represents a continuous scale.
Box 2: No -
K-Means Clustering - Because the K-means algorithm is an unsupervised learning method, a label column is optional. If your data includes a label, you can use the label values to guide selection of the clusters and optimize the model. If your data has no label, the algorithm creates clusters representing possible categories, based solely on the data.
Box 3: No - For classification problems, the label column must contain either categorical values or discrete values. Some examples might be a yes/no rating, a disease classification code or name, or an income group. If you pick a noncategorical column, the component will return an error during training. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/train-model https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/k-means-clustering
C) For the classification, labels must be numeric. Why No? Labels must be a number, even if it's just categorical values 0,1,2. If we have classified labels like YES/ NO, we still have to convert them into numbers, it's called label encoding, right?
I made a simple gender predictor (binary classification) in python with scikitlearn and I had to encode both the labels and features into numeric vaules. I was unable to get it working with categorial variables. I think some training algorithms might support non numeric training data but the simple scikitlearn one I was using didn't.
the sentence says: for a classification model labels MUST be numeric! It exclude non numeric labels and of course it's wrong. So the given answer is correct
--For a regression model, labels must be numeric.
Yes. This statement is accurate. In a regression model, the labels (target variable) must be numeric because regression aims to predict a continuous numerical value.
--For a clustering model, labels must be used.
No. This statement is not accurate. Clustering is an unsupervised learning technique that doesn't require predefined labels. It groups data based on similarity without using known labels.
--For a classification model, labels must be numeric.
Yes. This statement is generally accurate. In a classification model, the labels represent different classes or categories, and they are typically assigned numeric values (e.g., 0, 1, 2) to represent the classes. However, in some cases, categorical labels may also be used, but they are often encoded into numeric values for modeling.
Sorry, but you should expand your knowledge and then go back here.
upvoted 2 times
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