Multiclass classification, also known as multinomial classification, is the difficulty of classifying events into one of three or more categories in statistical classification and machine learning.
The challenge of categorizing occurrences into one of three or more classes in machine learning and statistical classification is known as multiclass classification or multinomial classification (classifying instances into one of two classes is called binary classification).
In multi-class classification, accuracy is among the most widely used metrics, and it may be calculated straight from the confusion matrix.
The Accuracy formula takes into account the total of True Positive and True Negative components in the numerator and the total of all entries in the confusion matrix in the denominator.
The complete question is:
Consider a multiclass classification problem where the label Y takes K different values and the feature X is a p-dimensional numerical vector. If we want to train a QDA classifier, what are the parameters we need to estimate? Circle all that apply.
A K-by-p matrix, with the (k,j)-th entry representing the variance of the j-th feature from class k.
A p-dim vector with the j-th entry being the mean of the j-th feature.
A K-dimensional probability vector, which represents the frequency of each of the K classes.
A p-by-K matrix with the j-th column (a p-dim vector) representing the mean of feature X from class j.
A p-dim vector with the j-th entry being the variance of the j-th feature.
A p-by-p covariance matrix.
K p-by-p covariance matrices, one for each class.
Therefore the answer is A p-by-K matrix with the j-th column (a p-dim vector) representing the mean of feature X from class j.
A K-dimensional probability vector, which represents the frequency of each of the K classes.
K p-by-p covariance matrices, one for each class.
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