The use of Bayesian techniques does not eliminate the need for sensitivity tests. Naive Bayes, on the other hand, is a simple algorithm that is very fast. Strengths: Linear regression is straightforward to understand and explain, and can be regularized to avoid overfitting.
Strengths and Weaknesses of Naive Bayes The main strengths are: Easy and quick way to predict classes, both in binary and multiclass classification problems. A Naive Bayes classifier can be trained to classify patterns involving thousands of attributes and applied to thousands of patterns.
In addition, linear models can be updated easily with new data using stochastic gradient descent.
In contrast, the Bayesian approach to stock assessment explicitly allows for weighting across alternative hypotheses through Bayes Theorem.
Each algorithm has its strengths and weaknesses.
Weaknesses: Linear regression performs poorly when there are non-linear relationships. They are not naturally flexible enough to capture more complex patterns, and adding the right interaction …
In the cases that the independence assumption fits, the algorithm performs better compared to …
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