Categories classification model is a model that predicts the class(category) of a given data pointer. The model is trained with labeled training data and then classifies new data into categories. Categories are sometimes to referred to as label or class. Classification prediction modeling is the approximation of a mapping function(f) from an input variable(X) to a discrete output variable(y).
Categories classification model is applied in many real life cases, such as credit approval, medical diagnostics, and targeted marketing.
1. Industry risk prediction
2. Email spam classification
3. Cancer cell identification
4. Bank loan prediction
- Binary Classification
Binary classification is a classification of elements into two categories. Spam identification, gender classification, and churn prediction are examples of binary classification.
- Multiclass Classification
Multiclass Classification is a classification of elements into three or more categories. Each sample is assigned to only one target label. Face classification and plant classification are examples of multiclass classification.
- Multi-label Classification
Multi-label Classification is a classification of elements into three or more categories, but each data could be classified to more than one classes. For example in object recognition, there could be multiple objects in an image. And from the image, a multi-label classification model could recognize multiple objects like 'bike', 'person', and 'apple' and classify the image into multiple classes.
- Imbalanced Classification
Imbalanced Classification is a classification where the distribution of examples across the known classes is biased or skewed. In this classification, the majority part of the train data set is classified normal while only a few are classified abnormal. Fraud detection, outlier detection, and medical diagnostic test are examples of imbalanced classification.
CLICK AI provides codeless artificial intelligence model development experience for users. With a just few clicks, users can develop machine learning models at a professional level through the automated process, and can build practical models to maximize business revenues.
Data is analyzed to gain market insights, and data analysis results are available for users to easily understand. Artificial intelligence could be trained with data sets to find out data patterns and make predictions based on it. You can automate the entire data process.
The following is an example of airline customer satisfaction classification.