In the field of machine learning, regression algorithms and classification algorithms are two important topics that lay a good foundation for people who want to advance their careers in the fields of Data Science or Machine Learning. Regression algorithms are methods that predict a continuous output (e.g., the price of a house), and classification algorithms are methods that predict labels or classes for the given input data (e.g., spam or not-spam).

For the purposes of this article, we will focus on machine learning models for classification.

Should I Use a Linear or Non-Linear Classification Algorithm?

To segregate the input data into different classes, we need a hyperplane or a decision boundary that will help classify the input data points. If the input data can be segregated by drawing a straight line, then we can use a linear model, and if the input data cannot be segregated with a straight line, then we would need to use a non-linear model.

What Types of Algorithms Can I Use for Classification?

• Logistic Regression: In this algorithm, the log odds of the outcome are modeled as a linear combination of the input data or variables. It is vulnerable to overfitting.
• Linear Support Vector Machines (SVM): Linear SVM is also used for classification and works well for text-related input data. The risk of overfitting is less in SVM.
• Decision Tree Classifier: This is a non-linear tree-based algorithm – a series of conditional statements that segregate input data into similar groups. It starts with a root node and then branches off just like a tree into decision nodes and leaf nodes. It is prone to overfitting.
• Random Forest Classifier: This non-linear algorithm consists of a large number of individual decision trees that operate as an ensemble. All the individual trees collectively vote for the outcome or prediction. The risk of overfitting is less in a random forest.
• XGBoost Classifier: A non-linear algorithm, an XGBoost Classifier features a large number of individual decision trees that operate as an ensemble. The trees are built in a sequence such that each subsequent tree reduces the error of the previous tree. Overfitting can be avoided by using an early stopping approach.

What Metrics Should I Use to Evaluate Classifier Model Performance?

There are several metrics that you can use to evaluate a classifier’s performance based on the problem it is trying to solve. The most common metrics used are precision, recall, F1 score, and accuracy. In some instances, precision might be more important than recall or vice versa.

Conclusion

In summary, selecting the right classification model is a trade-off between performance, execution time of models, and scalability. Furthermore, parameter tuning should be given attention to further optimize model performance.