Which machine learning classifier to choose, in general?

Question

There are a lot of classifiers to choose from but how to understand which classifier to use and when? Is there any common approach or logic I should follow ?

Answer 1

@Anisha, There is no specific logic for choosing a classifier but following are some preferrable classification technique which is commonly used depending on your requirement:

 Decision Tree-Decision tree is one of the most popular tools for classification and prediction. It is a flowchart of a tree structure where each note represents a test, each branch represents the outcome of a test and each terminal node hold a class label.

Rule-based classifiers-The term rule-based classification can be used to refer to any classification scheme that makes use of IF-THEN rules for class prediction.

 SMO (SVM)-A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, this algorithm outputs an optimal hyperplane which categorizes new examples.

 Naive Bayes-Naive Bayes predicts membership probabilities for each class such as probability that given record or data point belongs to a particular class .

Neural Networks-A neural network consists of units (neurons), arranged in layers, which convert an input vector into some output.

 For more details you may visit:https://scikit-learn.org/stable/tutorial/machine_learning_map/