Category: By using MLlib, you can create classification models to predict the category of data instances, such as spam email detection and sentiment analysis.
Return: MLlib can be used to build regression models to predict numerical results, such as stock price forecasting and housing price prediction.
Clustering: MLlib can assist users in conducting cluster analysis on data, uncovering hidden patterns and correlations such as user segmentation and market segmentation.
Recommendation system: MLlib can be used to build recommendation systems that recommend personalized products or services based on the user’s historical behavior and preferences.
Collaborative Filtering: MLlib can be used to implement collaborative filtering algorithms, helping users discover similarities and common interests between users, thereby improving the accuracy of recommendations.
Feature extraction and transformation: MLlib can be used for extracting and transforming features, assisting users in converting raw data into features that are more suitable for machine learning algorithms to process.
Model evaluation and optimization: MLlib offers a variety of tools for evaluating and optimizing models, helping users choose the most suitable model for their data and fine-tune the model.
