defining the experiencee at this pointyou know that any learning algorithm typically needs data to learn over time and perform specific taskwhich we named as the process of consuming dataset that consists of data samples or data points such that learning algorithm or model learns inherent patterns is defined as the experiencee which is gained by the learning algorithm any experience that the algorithm gains is from data samples or data points and this can be at any point of time you can feed it data samples in one go using historical data or even supply fresh data samples whenever they are acquired thusthe idea of model or algorithm gaining experience usually occurs as an iterative processalso known as training the model you could think of the model to be an entity just like human being which gains knowledge or experience through data points by observing and learning more and more about various attributesrelationships and patterns present in the data of coursethere are various forms and ways of learning and gaining experience including supervisedunsupervisedand reinforcement learning but we will discuss learning methods in future section for nowtake step back and remember the analogy we drew that when machine truly learnsit is based on data which is fed to it from time to time thus allowing it to gain experience and knowledge about the task to be solvedsuch that it can used this experienceeto predict or solve the same tasktin the future for previously unseen data points defining the performancep let' say we have machine learning algorithm that is supposed to perform tasktand is gaining experienceewith data points over period of time but how do we know if it' performing well or behaving the way it is supposed to behavethis is where the performancepof the model comes into the picture the performancepis usually quantitative measure or metric that' used to see how well the algorithm or model is performing the tasktwith experiencee while performance metrics are usually standard metrics that have been established after years of research and developmenteach metric is usually computed specific to the tasktwhich we are trying to solve at any given point of time typical performance measures include accuracyprecisionrecallf scoresensitivityspecificityerror ratemisclassification rateand many more performance measures are usually evaluated on training data samples (used by the algorithm to gain experienceeas well as data samples which it has not seen or learned from beforewhich are usually known as validation and test data samples the idea behind this is to generalize the algorithm so that it doesn' become too biased only on the training data points and performs well in the future on newer data points more on trainingvalidationand test data will be discussed when we talk about model building and validation while solving any machine learning problemmost of the timesthe choice of performance measurepis either accuracyf scoreprecisionand recall while this is true in most scenariosyou should always remember that sometimes it is difficult to choose performance measures that will accurately be able to give us an idea of how well the algorithm is performing based on the actual behavior or outcome which is expected from it simple example would be that sometimes we would want to penalize misclassification or false positives more than correct hits or predictions in such scenariowe might need to use modified cost function or priors such that we give scope to sacrifice hit rate or overall accuracy for more accurate predictions with lesser false positives real-world example would be an intelligent system that predicts if we should give loan to customer it' better to build the system in such way that it is more cautious against giving loan than denying one the simple reason is because one big mistake of giving loan to potential defaulter can lead to huge losses as compared to denying several smaller loans to potential customers to concludeyou need to take into account all parameters and attributes involved in tasktsuch that you can decide on the right performance measurespfor your system