Contents
What is the impact of outliers on dataset?
Effect of outliers on a data set It increases the error variance and reduces the power of statistical tests. They can cause bias and/or influence estimates. They can also impact the basic assumption of regression as well as other statistical models.
Is naive Bayes affected by Multicollinearity?
Answer: Multi collinearity is a condition when two or more variables carry almost the same information. This condition will allow the model to be biased towards a variable. So, multi collinearity does not affect the Naive Bayes.
What is the effect of outlier on mean?
Outlier An extreme value in a set of data which is much higher or lower than the other numbers. Outliers affect the mean value of the data but have little effect on the median or mode of a given set of data.
Why Naive Bayes correlated features affects performance?
Yes, it will affect the performance of Naive Bayes. It is called Naive because it assumes an independence between the features, which in practice is rarely the case. However, it’s shown to be fairly robust to this and to be able to perform well on real-world problems.
Why does the Naive Bayes classifier perform well even when the features are correlated?
The reason: The Naive Bayes classifier’s conditional independence assumption, namely the assumption that features are independent of one another when conditioned upon class labels, is rarely accurate. Features often depend on one another non-trivial amounts, meaning multiple features often contain similar signals.
Which is most affected by outliers?
The mean is affected by the outliers since it includes all the values in the distribution and the outlier can increase or decrease the mean value but it is not as susceptible as the range. By definition, the mean is the sum of the value of each observation in a dataset divided by the number of observations.
When to use the naive Bayes classification algorithm?
Naive Bayes Classification Algorithm is highly efficient and not biased by the Outliers. Naive Bayes algorithm works on Non-Linear data problems and used when we want to rank our predictions by their Probability. That’s all for the Naive Bayes Machine learning algorithm.
Are there any real world uses for naive Bayes?
In spite of their apparently over-simplified assumptions, naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They require a small amount of training data to estimate the necessary parameters.
What kind of distribution does a Gaussian naive Baye have?
In Gaussian Naive Bayes, continuous values associated with each feature are assumed to be distributed according to a Gaussian distribution. A Gaussian distribution is also called Normal distribution. When plotted, it gives a bell shaped curve which is symmetric about the mean of the feature values as shown below:
What is the formula for naive Bayes theorem?
Now, before moving to the formula for Naive Bayes, it is important to know about Bayes’ theorem. Bayes’ Theorem finds the probability of an event occurring given the probability of another event that has already occurred. Bayes’ theorem is stated mathematically as the following equation: