Classification in Machine Learning

Introduction

Classification is the process of predicting the class of given data points. Classes are sometimes called targets, labels or categories. Classification predictive modeling is the task of approximating a mapping function (F) from input variables (X) to discrete output variables (Y)

What is Classification?

In machine Learning, Classification refers to training a model on a labeled dataset to assign data points to classes or to classify new data points.

The task of classification and the most common are:

Binary, multi-class, Multi-label and imbalanced classification.

Types of Classification

There are a lot more types of classification algorithms not covered in this Article. Here are few:

o K-Nearest Neighbors

o Logistics Regression

o Decision Tree

o Random Forest

o Support Vector Classification

Decision Tree

decision tree

The decision tree classification is based on the decision tree algorithm. Decision tree are predictive models that use simple binary rules to predict the value of a target variables. One good thing is Decision tree can be used for both classification and regression. They are models containing branches, nodes and leaves. They split a dataset into smaller parts containing similar elements. Decision tree fit a sine curve to the data to define the rules of the classification or regression.

Some decision trees classification algorithms are:

DecisionTreeClassifier in sklearn.tree

Four steps of Decision Tree

The steps in decision tree with consist of:

1. Define the problem area for which decision making is necessary

2. Draw a decision tree with all possible solutions and their consequences.

3. Input relevant variables with their respective probability values

4. Determine and allocate payoffs for each possible outcome

Conclusion

We have seen an overview of machine learning classification and covered the basic classification algorithms.

Happy Learning!

Abubakar Labaran Salisu

#ArewaDataScienceAcademy