In machine learning, training and evaluating models are very important steps. The following will introduce the training and evaluation process of the machine learning model, and how to divide the training set, verification set and test set.
How to train and evaluate machine learning models?
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Data preparation: Before starting the training, the data set needs to be prepared first. A dataset should contain input features and corresponding labels or target values. Ensuring the quality and completeness of datasets is critical for model training and evaluation.
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Divide training set, validation set and test set: In order to evaluate the performance and generalization ability of the model, it is often necessary to divide the data set into training set, validation set and test set. The division ratio can be adjusted according to the specific situation. Usually, 70% of the data is used for training, 10-15% of the data is used for verification, and 15-20% of the data is used for testing.
- Training Set: The data set used to train the model. The model tunes its own parameters by learning the relationship between features and labels on the training set.
- Validation Set: Used for hyperparameter selection and tuning of the model, as well as performance evaluation of the model. By evaluating the performance of the model on the validation set, appropriate hyperparameters, such as learning rate, regularization parameters, etc., can be selected according to the performance of the model on the validation set.
- Test Set (Test Set): used to finally evaluate the generalization ability and performance of the model. The test set is data that has not been used during model training and validation and is used to simulate the performance of the model in practical applications.
How to divide the training set, validation set and test set?
1. Model training: Model training refers to adjusting the parameters of the model by inputting the data of the training set so that it can learn the relationship between the input features and the labels. The training process usually includes the following steps:
- Initialize model parameters: According to the structure and type of the model, initialize the parameters of the model, such as weights and biases.
- Forward propagation: Input the training data into the model and calculate the prediction result of the model.
- Calculate the loss function: compare the difference between the prediction result of the model and the real label, and obtain the loss value of the model.
- Backpropagation: Based on the loss value, the gradient of the model parameters is calculated by the chain rule.
- Parameter update: Use an optimization algorithm (such as stochastic gradient descent) to update the parameters of the model according to the gradient information to reduce the loss value.
- Repeat the above steps: repeatedly iterate the samples in the training set until a predetermined stopping condition is reached, such as reaching the maximum number of iterations or the convergence of the loss function.
2. Model evaluation: During the training process, the validation set can be used to evaluate the performance of the model in order to adjust the hyperparameters and structure of the model. Commonly used evaluation indicators include accuracy rate, precision rate, recall rate, F1 score, etc. The specific selection of evaluation indicators depends on the characteristics and needs of the task. After the model training is finally completed, the test set is used to evaluate the generalization ability of the model. Through the evaluation results of the test set, the performance index of the model in practical application can be obtained, so as to judge the quality of the model.
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When training and evaluating a machine learning model, it is very important to divide the data set reasonably, select the appropriate evaluation index, and adjust the hyperparameters appropriately. At the same time, care should be taken to avoid excessive tuning on the test set to maintain the independence and objectivity of the test set. Through the above steps and precautions, the training and evaluation of the machine learning model can be effectively carried out, and the performance and generalization ability of the model can be improved.