classification introduction introduction to classification classification may be defined as the process of predicting class or category from observed values or given data points the categorized output can have the form such as "blackor "whiteor "spamor "no spammathematicallyclassification is the task of approximating mapping function (ffrom input variables (xto output variables (yit is basically belongs to the supervised machine learning in which targets are also provided along with the input data set an example of classification problem can be the spam detection in emails there can be only two categories of output"spamand "no spam"hence this is binary type classification to implement this classificationwe first need to train the classifier for this example"spamand "no spamemails would be used as the training data after successfully train the classifierit can be used to detect an unknown email types of learners in classification we have two types of learners in respective to classification problemslazy learners as the name suggestssuch kind of learners waits for the testing data to be appeared after storing the training data classification is done only after getting the testing data they spend less time on training but more time on predicting examples of lazy learners are knearest neighbor and case-based reasoning eager learners as opposite to lazy learnerseager learners construct classification model without waiting for the testing data to be appeared after storing the training data they spend more time on training but less time on predicting examples of eager learners are decision treesnaive bayes and artificial neural networks (annbuilding classifier in python scikit-learna python library for machine learning can be used to build classifier in python the steps for building classifier in python are as followsstep importing necessary python package for building classifier using scikit-learnwe need to import it we can import it by using following script