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Pattern Recognition
How is the accuracy defined?
accuracy = (TP + TN)/(TP +FP + TN +FN)
Pattern Recognition
What does specificity describe? How is defined?
Specificity describes how reliably negative samples are labeled as such:
specificity = TN/(TN +FP)
Pattern Recognition
What are otherwise we can look at a classifiers performance?
Another way of looking at classifier performance is the predictive value of a label.
As the name implies, this metric describes the probability of a sample actually belonging to class X if it was classified as such:
PPV = Positive Predictive Value = TP/(TP +FP)
NPV = Negative Predictive Value = TN/(TN +FN)
Pattern Recognition
How is F1-measure defined?
F1 measure = 2 ×precision × recall/(precision + recall)
Pattern Recognition
What is k-fold cross-validation? And why do we do it?
With e.g. k = 5, the data are split into 5 equal pieces. In the first fold, pieces 1–4 are used for training and piece 5 for testing; in the second fold, piece 4 is used for testing and 1–3, 5 for training; etc.
• every data point is tested exactly once
• but still expensive
We use it to generalize well on new data and to avoid overfitting on the available training data.
Pattern Recognition
What methods other than "k-fold cv" can we used to avoid overfitting and generalize well on new data?
Pattern Recognition
How does bias and variance error gets introduced?
Error due to model complexity is called the variance error. Error introduced due to some biases in the data is called bias error.
Pattern Recognition
Can you give an example of a classifier with high bias and high variance?
High bias means the data is being underfitted. The decision boundary is not usually complex enough. High variance happens due to overfitting, the decision boundary is more complex than what it should be.
High bias high variance happens when you fit a complex decision boundary that is also not fitting the training set correctly in several places.
Pattern Recognition
What are the advantages and disadvantages of using naive bayes for spam detection?
Pattern Recognition
What three types of outlier exists?
Pattern Recognition
Describe how isolation trees detect outliers.
Pattern Recognition
Briefly describe how Naive Bayes words. Where is it normally applied?
Naive Bayes is a supervised learning algorithm for classification so the task is to find the class of observation (data point) given the values of features. Naive Bayes classifier calculates the posterior probabilities using Bayes Theorem of it being a specific class when specific features appear. This classifier assumes the features (e.g. words as input) are independent, so the calculation of the class given the features is easier to compute.
It used for
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