overfitting risk
过拟合风险
avoid overfitting
避免过拟合
overfitting problem
过拟合问题
detect overfitting
检测过拟合
prevent overfitting
预防过拟合
overfitting data
过拟合数据
checking overfitting
检查过拟合
reducing overfitting
减少过拟合
prone to overfitting
容易过拟合
overfitting occurs
过拟合发生
the model suffered from overfitting and performed poorly on new data.
模型过拟合，在新数据上表现不佳。
we need to avoid overfitting during the training process.
我们需要在训练过程中避免过拟合。
regularization techniques can help prevent overfitting in machine learning.
正则化技术可以帮助防止机器学习中的过拟合。
overfitting occurs when a model learns the training data too well.
当模型过度学习训练数据时，就会发生过拟合。
cross-validation is a common method to detect overfitting.
交叉验证是检测过拟合的常用方法。
the risk of overfitting is higher with complex models.
复杂模型的过拟合风险更高。
we used dropout layers to mitigate overfitting in the neural network.
我们使用 dropout 层来减轻神经网络中的过拟合。
careful feature selection can reduce the likelihood of overfitting.
仔细选择特征可以降低过拟合的可能性。
the validation set helps us identify and address overfitting issues.
验证集帮助我们识别和解决过拟合问题。
early stopping is a strategy to prevent overfitting on the training data.
提前停止是一种防止在训练数据上过拟合的策略。
we evaluated the model's performance to check for overfitting.
我们评估了模型的性能以检查是否存在过拟合。
overfitting risk
过拟合风险
avoid overfitting
避免过拟合
overfitting problem
过拟合问题
detect overfitting
检测过拟合
prevent overfitting
预防过拟合
overfitting data
过拟合数据
checking overfitting
检查过拟合
reducing overfitting
减少过拟合
prone to overfitting
容易过拟合
overfitting occurs
过拟合发生
the model suffered from overfitting and performed poorly on new data.
模型过拟合，在新数据上表现不佳。
we need to avoid overfitting during the training process.
我们需要在训练过程中避免过拟合。
regularization techniques can help prevent overfitting in machine learning.
正则化技术可以帮助防止机器学习中的过拟合。
overfitting occurs when a model learns the training data too well.
当模型过度学习训练数据时，就会发生过拟合。
cross-validation is a common method to detect overfitting.
交叉验证是检测过拟合的常用方法。
the risk of overfitting is higher with complex models.
复杂模型的过拟合风险更高。
we used dropout layers to mitigate overfitting in the neural network.
我们使用 dropout 层来减轻神经网络中的过拟合。
careful feature selection can reduce the likelihood of overfitting.
仔细选择特征可以降低过拟合的可能性。
the validation set helps us identify and address overfitting issues.
验证集帮助我们识别和解决过拟合问题。
early stopping is a strategy to prevent overfitting on the training data.
提前停止是一种防止在训练数据上过拟合的策略。
we evaluated the model's performance to check for overfitting.
我们评估了模型的性能以检查是否存在过拟合。
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