Machine learning model training method and apparatus, server, and storage medium

A machine learning model training apparatus, comprising:

• a memory;
• and
• one or more processors configured to:
• train a machine learning model using features of each sample in a training set based on an initial first weight of each sample and an initial second weight of each sample;
• in one iteration of training the machine learning model,
• determine a first sample set comprising a sample whose corresponding target variable is incorrectly predicted, and a second sample set comprising a sample whose corresponding target variable is correctly predicted, based on a predicted loss of each sample in the training set;
• determine an overall predicted loss of the first sample set based on the predicted loss and a corresponding first weight of each sample in the first sample set;
• update the first weight and a second weight of each sample in the first sample set based on the overall predicted loss of the first sample set;
• and
• input the updated second weight of each sample in the training set, the features of each sample in the training set, and the target variable of each sample in the training set to the machine learning model, and initiate a next iteration of training the machine learning model.

A non-transitory storage medium, storing an executable program, when being executed by a processor, the executable program causes the processor to perform:

• training a machine learning model using features of each sample in a training set based on an initial first weight of each sample and an initial second weight of each sample;
• in one iteration of training the machine learning model,
• determining a first sample set comprising a sample whose corresponding target variable is incorrectly predicted, and a second sample set comprising a sample whose corresponding target variable is correctly predicted, based on a predicted loss of each sample in the training set;
• determining an overall predicted loss of the first sample set based on the predicted loss and a corresponding first weight of each sample in the first sample set;
• updating the first weight and a second weight of each sample in the first sample set based on the overall predicted loss of the first sample set;
• and
• inputting the updated second weight of each sample in the training set, the features of each sample in the training set, and the target variable of each sample in the training set to the machine learning model, and initiating a next iteration of training the machine learning model.