Linear Regression & Naïve Bayes
6 important questions on Linear Regression & Naïve Bayes
What are the objectives for single/multiple regression?
1. Predictive: Detect the outcome value for new records, given their input values
a. Given a new value for X, estimate the value for Y
2. Explanatory or descriptive: Quantifying / explaining the average effect of inputs on an outcome. Data are treated as a random sample from a larger population of interest.
a. Generate statements useful for decision making
What are possible issues with regression?
the model learns the existing data too well.
2. Underfitting: Model can’t accurately capture the data dependencies, Fails to identify effects
supported by the data, Usually this happens due to the model’s simplicity.
What is the consequence of overfitting?
predictions.
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What is the consequence of underfitting?
data
What are the negative aspects of Naïve Bayes?
- Requires a very large number of records to obtain good results
- Independence assumption may not hold for some attributes
What are the advantages of Naïve Bayes?
- It has a probabilistic interpretation
- It is able to deal with a small training set
- It is able to deal with missing values
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