What is Bayes optimal error rate?
In statistical classification, Bayes error rate is the lowest possible error rate for any classifier of a random outcome (into, for example, one of two categories) and is analogous to the irreducible error. The Bayes error rate finds important use in the study of patterns and machine learning techniques.
How do you get Bayes error rate?
For a given feature space, the Bayes error rate provides a lower bound on the error rate that can be achieved by any pattern classifier acting on that space, or on derived features selected or extracted from that space [14, 20, 25, 67]. This rate is greater than zero whenever the class distributions overlap.
How do you find the accuracy of a Naive Bayes classifier?
Naive Bayes classifier calculates the probability of an event in the following steps:
- Step 1: Calculate the prior probability for given class labels.
- Step 2: Find Likelihood probability with each attribute for each class.
- Step 3: Put these value in Bayes Formula and calculate posterior probability.
How do you calculate error rate in classification?
Error rate (ERR) is calculated as the number of all incorrect predictions divided by the total number of the dataset. The best error rate is 0.0, whereas the worst is 1.0. Error rate is calculated as the total number of two incorrect predictions (FN + FP) divided by the total number of a dataset (P + N).
What is a weak classifier a classifier with an error rate?
Weak learners are algorithms whose error rate is slightly under 50% as illustrated below : A weak classifier, achieving just under 50% error rate.
Why Bayes classifier is optimal?
It can be shown that of all classifiers, the Optimal Bayes classifier is the one that will have the lowest probability of miss classifying an observation, i.e. the lowest probability of error. So if we know the posterior distribution, then using the Bayes classifier is as good as it gets.
What is minimum error rate classification?
The Minimum Classification Error (MCE) criterion is a well-known criterion in pattern classification systems. The aim of MCE training is to minimize the resulting classification error when trying to classify a new data set. Usually, these classification systems use some form of statistical model to describe the data.
What is accuracy in Naive Bayes?
one of the two other methods. The average accuracy. for C4. 5 is 81.91%, for Naive-Bayes it is 81.69%, and for NBTree it is 84.47%.
How is Naive Bayes probability calculated?
The conditional probability can be calculated using the joint probability, although it would be intractable. Bayes Theorem provides a principled way for calculating the conditional probability. The simple form of the calculation for Bayes Theorem is as follows: P(A|B) = P(B|A) * P(A) / P(B)
What is one way to estimate the expected error of a classifier?
The error rate estimate, e0, is computed by counting all the errors and dividing the sum by the total number of test samples used.
What are the four error measurement used as classification metrics?
To illustrate, we can see how the 4 classification metrics are calculated (TP, FP, FN, TN), and our predicted value compared to the actual value in a confusion matrix is clearly presented in the below confusion matrix table.
What is the difference between a weak classifier and a strong classifier?
Weak learners are models that perform slightly better than random guessing. Strong learners are models that have arbitrarily good accuracy. Weak and strong learners are tools from computational learning theory and provide the basis for the development of the boosting class of ensemble methods.