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AI reject model for Content and Comment Https://www.binance.com/zh-TC/square/post/16183431638882
AI reject model for Content and Comment

Https://www.binance.com/zh-TC/square/post/16183431638882
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Take a look to see if you've made the list: Nickname - Weekly reward D****r -173.86 FDUSD 四**律- 151.39 FDUSD 币****啦- 124.09 FDUSD D****C -115.69 FDUSD O****歌 - 108.74FDUSD
Take a look to see if you've made the list:
Nickname - Weekly reward
D****r -173.86 FDUSD
四**律- 151.39 FDUSD
币****啦- 124.09 FDUSD
D****C -115.69 FDUSD
O****歌 - 108.74FDUSD
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龚有柴GongYouchai
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The performance of A-shares has attracted widespread attention recently. Due to the previous rapid rise, many people failed to keep up with the pace and were left behind by the market. Then they chased in at a high level and were directly trapped at the top of the mountain.
Cryptocurrency has many similarities with the stock market. In fact, we should not expect the market to soar by thousands or even tens of thousands of points overnight. This expectation is neither realistic nor healthy. The most ideal market trend is a steady bull market, with prices rising gradually and gradually washing out, increasing the holding costs of investors.

Recalling the process of Bitcoin gradually rising from 10,000 points to 30,000 points, and then from 30,000 points to 60,000 points, each step was accompanied by washing out. Now that the price of Bitcoin has reached 60,000 points, the next step may be to continue washing out, and the target may be 90,000 points or even higher. In this process, some people will inevitably suffer losses, and some people will choose to cut their losses and leave the market. This is the norm in the market.
My advice to everyone is that Bitcoin should be your cornerstone in the cryptocurrency market. No matter how the altcoins fluctuate, holding Bitcoin is the key. At the same time, don't hold on to it, but be flexible. We are optimistic about the prospects of the cryptocurrency market and Bitcoin in the long term, but this does not prevent us from short-term swing operations.
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In recommendation algorithms, commonly used evaluation indicators include Accuracy, Precision, Recall, and AUC. These indicators are used to evaluate the performance and effect of recommendation algorithms. 1. Accuracy: Accuracy refers to the proportion of correct predictions among all predicted results. In a recommendation system, accuracy indicates how many of the predicted recommended results are actually of interest to the user. The accuracy ranges from 0 to 1, and the higher the value, the more accurate the recommendation result. 2. Precision: Precision refers to the proportion of truly positive samples among all predicted positive samples. In a recommendation system, precision indicates how many of the items recommended to the user are actually of interest to the user. The precision ranges from 0 to 1, and the higher the value, the higher the proportion of items of interest to the user included in the recommendation results. 3. Recall: Recall refers to the proportion of successfully predicted positive samples among all true positive samples. In a recommendation system, recall indicates how many items that the user is actually interested in are successfully recommended to the user. The recall rate ranges from 0 to 1. The higher the value, the higher the proportion of items of interest to the user in the recommended results. 4. AUC (Area Under the Curve): AUC is an indicator used to evaluate the ranking performance of the recommendation system. It indicates the ranking ability of the recommendation algorithm for positive and negative samples, that is, the probability that the recommendation results can be correctly sorted among all positive and negative sample pairs. The AUC value ranges from 0.5 to 1. The closer the value is to 1, the better the sorting ability. It should be noted that the interpretation and use of these evaluation indicators may vary depending on the specific recommendation algorithm and application scenario. In specific applications, appropriate evaluation indicators can be selected according to business needs and algorithm characteristics to evaluate the performance of the recommendation algorithm.
In recommendation algorithms, commonly used evaluation indicators include Accuracy, Precision, Recall, and AUC. These indicators are used to evaluate the performance and effect of recommendation algorithms.
1. Accuracy: Accuracy refers to the proportion of correct predictions among all predicted results. In a recommendation system, accuracy indicates how many of the predicted recommended results are actually of interest to the user. The accuracy ranges from 0 to 1, and the higher the value, the more accurate the recommendation result.
2. Precision: Precision refers to the proportion of truly positive samples among all predicted positive samples. In a recommendation system, precision indicates how many of the items recommended to the user are actually of interest to the user. The precision ranges from 0 to 1, and the higher the value, the higher the proportion of items of interest to the user included in the recommendation results.
3. Recall: Recall refers to the proportion of successfully predicted positive samples among all true positive samples. In a recommendation system, recall indicates how many items that the user is actually interested in are successfully recommended to the user. The recall rate ranges from 0 to 1. The higher the value, the higher the proportion of items of interest to the user in the recommended results.
4. AUC (Area Under the Curve): AUC is an indicator used to evaluate the ranking performance of the recommendation system. It indicates the ranking ability of the recommendation algorithm for positive and negative samples, that is, the probability that the recommendation results can be correctly sorted among all positive and negative sample pairs. The AUC value ranges from 0.5 to 1. The closer the value is to 1, the better the sorting ability.
It should be noted that the interpretation and use of these evaluation indicators may vary depending on the specific recommendation algorithm and application scenario. In specific applications, appropriate evaluation indicators can be selected according to business needs and algorithm characteristics to evaluate the performance of the recommendation algorithm.
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