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You would like to build a classifier for an Autism early detection application. Each data point...

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Programming

You would like to build a classifier for an Autism earlydetection application. Each data point in the dataset represents apatient. Each patient is described by a set of attributes such asage, sex, ethnicity, communication and development figures, etc.You know from domain knowledge that autism is more prevalent inmales than females.

If the dataset you are using to build the classifier is noisy,contains redundant attributes and missing values. If you areconsidering a decision tree classifier and a k-nearest neighborclassifier, explain how each of these can handle the threementioned problems:

1. Noise

2. Missing Values

3. Redundant Attributes

Answer & Explanation Solved by verified expert
3.8 Ratings (737 Votes)
there are various method to handle these problem some methods are following 1 Noise you can use feature correlation heatmap features to find out correlation between feature and target variable you can select a group of features and apply cross    See Answer
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