Multiclass Classification has more than two classes or outputs. One example is when a model is used to determine the species depicted in an encyclopedia photograph. This is because each image can be assigned to multiple species. Additionally, a sample must only include one class.
Multiclass classification is perhaps the most frequent machine learning job, excluding regression. In classification, we design a machine learning model to determine which of the K different classes some previously unobserved data belongs to after being presented with a large number of training samples. By analyzing the training dataset, the model discovers patterns unique to each class, which it then employs to forecast the membership of upcoming data.
In contrast to other animals lacking whiskers or pointed ears, photos of cats may all have pointed ears and whiskers, making it easier for the model to recognize images of cats in the future.
The method you use and train your dataset is key in obtaining useful insights, regardless of your level of expertise in machine learning or data science.
Multiple class categorization problems have several possible solutions:
We could generate a binary variable for each class and make predictions for each using binary classification, and then combine the findings. This, however, is not the best option if we have a large number of classes because it is computationally intensive. One-vs-all or all-vs-all reduction methods can be employed with this binary classifier for multiclass data. You can use decision tree techniques and logistic regression for multiclass classification.
To handle this particular problem, you can use a machine learning algorithm for multiclass classification like Neural Networks, Naive Bayes, and SVM.
You can also use multi-layer modeling, first classifying them into several categories before using additional modeling strategies on top of that.