It includes applications of supervised learning such as binary classification and multi-class classification!
Supervised learning algorithms use a known input dataset (training dataset) and the known responses (outputs) for that data to train a model, enabling the model to make reasonable predictions for new input data. When there is existing response (output) data for the event you are trying to predict, supervised learning is used. The materials include methods of supervised learning, as well as binary classification, multi-class classification, and general classification algorithms. [Contents] ■ When to consider supervised learning ■ Methods of supervised learning ■ Choosing the appropriate algorithm ■ Binary classification and multi-class classification ■ General classification algorithms *For more details, please refer to the PDF materials or feel free to contact us.
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【Other Published Content】 ■General Regression Algorithms ■Model Improvement ■Feature Selection ■Feature Transformation ■Hyperparameter Tuning ■References *For more details, please refer to the PDF document or feel free to contact us.
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For more details, please refer to the PDF document or feel free to contact us.
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MathWorks is a leading company in numerical analysis software for engineers and researchers. Engineers and scientists around the world use MathWorks products as tools to accelerate discovery, innovation, and development. Please feel free to contact us if you have any inquiries.