The Classification method is a Supervised Learning approach that uses training data to identify the category of fresh observations. A software in Classification learns from a given dataset or observations and then classifies additional observations into one of many classes or groupings. For example, Yes or No, 0 or 1, Spam or Not Spam, cat or dog, and so on. Classes can also be referred to as targets/labels or categories. In contrast to regression, the outcome variable of Classification is a category rather than a value, such as "Green or Blue", "fruit or Animal", and so on. Because the Classification method is a Supervised learning approach, it requires labeled input data, which implies it comprises input and output.
A discrete output function (y) is transferred to an input variable in a classification method (x). y=f(x), where y represents categorical output Email Spam Detector is the greatest example of an ML classification method. The Classification algorithm's main purpose is to determine the category of a given dataset, and these algorithms are mostly used to forecast the output for categorical data. The graphic below might help you better understand classification methods. There are two classes depicted in the figure below: class A and class B. These classes have features that are comparable to one another but not to other classes. See the figure below:
The algorithm which implements the classification on a dataset is known as a classifier. There are two types of Classifications: Binary Classifier: If the classification problem has only two possible outcomes, then it is called as Binary Classifier. Examples: YES or NO, MALE or FEMALE, SPAM or NOT SPAM, CAT or DOG, etc. Multi-class Classifier: If a classification problem has more than two outcomes, then it is called as Multi-class Classifier. Example: Classifications of types of crops, Classification of types of music.
Learners in Classification Problems:
In the classification problems, there are two types of learners: Lazy Learners: Lazy Learner firstly stores the training dataset and wait until it receives the test dataset. In Lazy learner case, classification is done on the basis of the most related data stored in the training dataset. It takes less time in training but more time for predictions. Example: K-NN algorithm, Case-based reasoning. Eager Learners:Eager Learners develop a classification model based on a training dataset before receiving a test dataset. Opposite to Lazy learners, Eager Learner takes more time in learning, and less time in prediction. Example: Decision Trees, Naïve Bayes, ANN.
Types of ML Classification Algorithms:
Classification Algorithms can be further divided into the Mainly two category: Linear Models 1) Logistic Regression. 2) Support Vector Machines. Non-linear Models 1) K-Nearest Neighbours. 2) Kernel SVM. 3) Naïve Bayes. 4) Decision Tree Classification. 5) Random Forest Classification.
Evaluating a Classification model: Once our model is completed, it is necessary to evaluate its performance; either it is a Classification or Regression model. So for evaluating a Classification model, we have the following ways: 1. Log Loss or Cross-Entropy Loss. 2. Confusion Matrix. 3. AUC-ROC curve.
Use cases of Classification Algorithms
Classification algorithms can be used in different places. Below are some popular use cases of Classification Algorithms: Email Spam Detection. Speech Recognition. Identifications of Cancer tumor cells. Drugs Classification. Biometric Identification, etc.