classification regression let' look at these two machine learning tasks and observe the subset of supervised learning methods that are best suited for tackling these tasks classification the classification based tasks are sub-field under supervised machine learningwhere the key objective is to predict output labels or responses that are categorical in nature for input data based on what the model has learned in the training phase output labels here are also known as classes or class labels are these are categorical in nature meaning they are unordered and discrete values thuseach output response belongs to specific discrete class or category suppose we take real-world example of predicting the weather let' keep it simple and say we are trying to predict if the weather is sunny or rainy based on multiple input data samples consisting of attributes or features like humiditytemperaturepressureand precipitation since the prediction can be either sunny or rainythere are total of two distinct classes in totalhence this problem can also be termed as binary classification problem figure - depicts the binary weather classification task of predicting weather as either sunny or rainy based on training the supervised model on input data samples having feature vectors(precipitationhumiditypressureand temperaturefor each data sample/observation and their corresponding class labels as either sunny or rainy figure - supervised learningbinary classification for weather prediction task where the total number of distinct classes is more than two becomes multi-class classification problem where each prediction response can be any one of the probable classes from this set simple example would be trying to predict numeric digits from scanned handwritten images in this case it becomes -class classification problem because the output class label for any image can be any digit from in