In machine learning, classification is a supervised learning concept which basically categorizes a set of data into classes. The most common classification problems are – speech recognition, face detection, handwriting recognition, document classification, etc.
The classification predictive modeling is the task of approximating the mapping function from input variables to discrete output variables. The main goal is to identify which class/category the new data will fall into.
Classification Terminologies In Machine Learning
Classifier – It is an algorithm that is used to map the input data to a specific category.
Classification Model – The model predicts or draws a conclusion to the input data given for training, it will predict the class or category for the data.
Feature – A feature is an individual measurable property of the phenomenon being observed.
Binary Classification – It is a type of classification with two outcomes, for eg – either true or false.
Multi-Class Classification – The classification with more than two classes, in multi-class classification each sample is assigned to one and only one label or target.
Multi-label Classification – This is a type of classification where each sample is assigned to a set of labels or targets.
Initialize – It is to assign the classifier to be used for the
Train the Classifier – Each classifier in sci-kit learn uses the fit(X, y) method to fit the model for training the train X and train label y.
Predict the Target – For an unlabeled observation X, the predict(X) method returns predicted label y.
Evaluate – This basically means the evaluation of the model i.e classification report, accuracy score, etc.