Why does the amount of training data increase, but the accuracy decreases?

The possible reasons for the increase in the amount of training data and the decrease in accuracy are as follows:

1.Overfitting: As the amount of training data increases, the model can become overcomplex, resulting in poor performance on new data and overfitting.

This means that the model performs well on the training data, but does worse on new data that has not been seen. To solve this problem, you can try adding regularization terms, reducing the complexity of the model, or using a simpler model.

2.Data noise: When the training data contains noise, the model can be affected, resulting in a degraded performance on the test data.

Noise can be introduced due to errors in data acquisition, processing, or labeling. To reduce the impact of noise, the data can be pre-processed and cleaned, or a more robust algorithm can be used to process the noisy data.

3.Data imbalance: When the sample size of different categories in the training data is unbalanced, the model may perform poorly on some categories.

To solve this problem, you can try using data augmentation techniques to increase the sample size of a few categories, or using more advanced classification algorithms to deal with data imbalances.

4.Poor model selection: When an inappropriate model is selected, it can lead to poor performance on training data. In order to select the appropriate model, experiments and cross-validation can be performed to evaluate the performance of different models and select the model with the best performance.

5.Inadequate training: When training is inadequate, the model may not be able to learn patterns in the data adequately, resulting in a degraded performance on the test data.

To fully train your model, you can try increasing the amount of training data, increasing the training time, adjusting the learning rate, or using more advanced optimization algorithms to improve the training effect.

It should be noted that the above reasons are not absolute, and there may be a variety of factors that lead to an increase in the amount of training data and a decrease in the accuracy rate in practical applications. In practical applications, it is necessary to troubleshoot and solve problems according to specific situations.

When the sample size changes, the result of the operation will change, and generally speaking, the larger the sample size, the closer the result will be to the real situation. Your current situation means that the accuracy of the model to the actual situation is only so much, and the accuracy given when the sample size is small is not the closest to the real accuracy. Generally speaking, the main way to improve the accuracy of the model** is to add or replace variables, rather than to improve the accuracy by expanding the training sample size.

The reason is that you don't do enough practice questions, and you have to continue to do them. Find out the wrong questions, summarize the reasons for your mistakes, and gradually improve the speed and accuracy of answering questions.

Hello, the accuracy of the question type of the test should be improved by training item by item. You can.

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Let's do the exercises, check and fill in the gaps.

The GRE test is not a simple language proficiency word test, but a test of the level of language proficiency and logic. You said that it is entirely determined by the number of words, which is certainly not true.

However, the GRE fill-in-the-blank part of the Easy and Medium questions is like this, but if you want to get a high score, you still need language skills, that is, you can read sentences without ambiguity. Memorization of words is a necessary but not sufficient condition.

The first 80 are wrong 25, and the last 60 are wrong 35, I feel that I should be enough for the rail transit police, right? .The boy's argument guarantee should be about 50 points, and the woman should ask for more blessings.

Almost 55%, generally not particularly popular positions are enough, last year I was 29 comprehensive, there is no higher than me in our bureau, the next thing is to improve the argument.

Your accuracy rate is not very high, it's usually about 64%.

Hello, the experts of Zhongzheng Xingwei will answer for you!

In fact, as long as you are proficient in the problem solving skills in the chapter of quantitative relations, your score will generally not be low. It's best not to give up on this type of question, there are difficulties and easy, it's really not good to master the basic skills, and you can also win 40% of the questions. The score of each major question type is different, and it is still inconvenient to estimate the score just by looking at the accuracy.

You can go to the Zhongzheng Xing Test Platform to apply for the test paper to answer, and the system will give you a score.

If you still have questions, please feel free to ask questions to the Zhongzheng Xing Test and Zhongzheng Essay Preparation Platform!

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Usually do more questions, memorize more formulas, speed up the speed of reading, if it still does not improve, it is recommended that you go to the training to see the OAO teaching of Zhonggong I hope it can help you.

If you have any questions, please feel free to ask questions from Zhonggong Education Enterprises.

Do more questions, practice makes perfect, and the more you do, the more you feel.

Correct rate: The correct rate, which is the percentage of the total number of correct answers.

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