Classification In Machine Learning

Classification In Machine Learning

Classification is a process of categorizing a given set of data into classes, It can be performed on both structured or unstructured data. The process starts with predicting the class of given data points. The classes are often referred to as target, label or categories.

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.

Let us try to understand this with a simple example.

Heart disease detection can be identified as a classification problem, this is a binary classification since there can be only two classes i.e has heart disease or does not have heart disease. The classifier, in this case, needs training data to understand how the given input variables are related to the class. And once the classifier is trained accurately, it can be used to detect whether heart disease is there or not for a particular patient.

Since classification is a type of supervised learning, even the targets are also provided with the input data. Let us get familiar with the classification in machine learning terminologies.

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.

Types Of Learners In Classification

  • Lazy Learners – Lazy learners simply store the training data and wait until a testing data appears. The classification is done using the most related data in the stored training data. They have more predicting time compared to eager learners. Eg – k-nearest neighbor, case-based reasoning.

  • Eager Learners – Eager learners construct a classification model based on the given training data before getting data for predictions. It must be able to commit to a single hypothesis that will work for the entire space. Due to this, they take a lot of time in training and less time for a prediction. Eg – Decision Tree, Naive Bayes, Artificial Neural Networks.

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