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NEW QUESTION 24
You are building a binary classification model by using a supplied training set.
The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Penalize the classification
- B. Generate synthetic samples in the minority class
- C. Use accuracy as the evaluation metric of the model
- D. Normalize the training feature set
- E. Resample the dataset using undersampling or oversampling
Answer: A,B,E
Explanation:
A: Try Penalized Models
You can use the same algorithms but give them a different perspective on the problem.
Penalized classification imposes an additional cost on the model for making classification mistakes on the minority class during training. These penalties can bias the model to pay more attention to the minority class.
B: You can change the dataset that you use to build your predictive model to have more balanced data.
This change is called sampling your dataset and there are two main methods that you can use to even-up the classes:
* Consider testing under-sampling when you have an a lot data (tens- or hundreds of thousands of instances or more)
* Consider testing over-sampling when you don’t have a lot of data (tens of thousands of records or less) D: Try Generate Synthetic Samples A simple way to generate synthetic samples is to randomly sample the attributes from instances in the minority class.
References:
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
NEW QUESTION 25
You need to define a process for penalty event detection.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
NEW QUESTION 26
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are analyzing a numerical dataset which contains missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Remove the entire column that contains the missing data point.
Does the solution meet the goal?
- A. Yes
- B. No
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Use the Multiple Imputation by Chained Equations (MICE) method.
References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
NEW QUESTION 27
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