Machine Learning

Confusion matrix is a table that is used to measure the performance of the machine learning classification model(typically for supervised learning, in case of unsupervised learning it usually called the matching matrix) where output can be two or more classes. Each row of the confusion matrix represents the instances in a predicted class while each column represents the instance in an actual class or vice versa.

F1-Score or F-measure is an evaluation metric for a classification defined as the harmonic mean of precision and recall. It is a statistical measure of the accuracy of a test or model."

A hyperparameter is an entity of a learning algorithm, usually (but not always) having a finite numerical value.Parameters are variables that define the model, learned by the learning algorithm

Loss functions in machine learning are the functions that deal with the evaluation of how accurate the given prediction is made. If the prediction is made far away from the actual or true value i.e. prediction deviates more from actual value, then the loss function gives high numeric value.

The loss functions are defined on a single training example and the cost function is the average of the loss function for the entire training set" "