Unlike regression where we predict a continuous number, you use classification to Predict a Category. There is a wide variety of classification applications from medicine to marketing. Classification models include linear models like
Which model to choose for my problem?
- For Linear Problem - Choose Logistic Regression or SVM
- For Non-Linear Problem - K-NN, Naive Bayes, Decision Tree or Random Forest
- Logistic Regression
- When you want to rank your predictions by their probability.
- E.g. To rank your customers from the highest probability that they buy a certain product, to the lowest probability.
- SVM
- When you want to predict to which segment your customers belong to. (Male/Female, Orange/Apple etc)
- Naive Bayes
- When you want to rank your predictions by their probability.
- E.g. To rank your customers from the highest probability that they buy a certain product, to the lowest probability.
- Decision Tree when you want to have clear interpretation of your model results,
- Random Forest when you are just looking for high performance with less need for interpretation.
Hope this helps!!
- Logistic Regression, SVM
- K-Nearest Neighbors (K-NN)
- Support Vector Machine (SVM)
- Kernel SVM
- Naive Bayes
- Decision Tree Classification
- Random Forest Classification
Which model to choose for my problem?
- For Linear Problem - Choose Logistic Regression or SVM
- For Non-Linear Problem - K-NN, Naive Bayes, Decision Tree or Random Forest
- Logistic Regression
- When you want to rank your predictions by their probability.
- E.g. To rank your customers from the highest probability that they buy a certain product, to the lowest probability.
- SVM
- When you want to predict to which segment your customers belong to. (Male/Female, Orange/Apple etc)
- Naive Bayes
- When you want to rank your predictions by their probability.
- E.g. To rank your customers from the highest probability that they buy a certain product, to the lowest probability.
- Decision Tree when you want to have clear interpretation of your model results,
- Random Forest when you are just looking for high performance with less need for interpretation.
Hope this helps!!
Regards,
Arun Manglick
The article provides a clear and practical introduction to classification in machine learning, highlighting how classification algorithms can be used to assign data into meaningful categories based on learned patterns. I particularly appreciate the structured explanation of the concepts, as it makes an important supervised learning technique easier to understand for both beginners and practitioners. Overall, it is a useful resource for building a strong foundation in machine-learning classification and understanding its real-world applications.
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