underfitting risk
欠拟合风险
avoiding underfitting
避免欠拟合
susceptible to underfitting
易于欠拟合
underfitting problem
欠拟合问题
detecting underfitting
检测欠拟合
model underfitting
模型欠拟合
underfitting data
欠拟合数据
prevent underfitting
预防欠拟合
checking for underfitting
检查欠拟合
severe underfitting
严重的欠拟合
the model suffered from severe underfitting and failed to capture the underlying patterns.
模型存在严重的欠拟合现象，未能捕捉到潜在模式。
we noticed significant underfitting when evaluating the model on the test set.
在评估模型时，我们注意到明显的欠拟合现象。
underfitting often results from using a model that is too simple for the data.
欠拟合通常是由于使用过于简单的模型来处理数据所致。
to avoid underfitting, we increased the model complexity and added more features.
为了避免欠拟合，我们增加了模型的复杂性并添加了更多特征。
the linear regression model exhibited underfitting compared to the neural network.
与神经网络相比，线性回归模型表现出欠拟合的现象。
underfitting leads to poor performance on both training and test data.
欠拟合会导致训练数据和测试数据上的表现不佳。
we checked for underfitting by plotting the training and validation loss curves.
我们通过绘制训练和验证损失曲线来检查是否存在欠拟合。
regularization can sometimes exacerbate underfitting if applied too aggressively.
如果过度应用，正则化有时会加剧欠拟合的现象。
the goal is to find a balance and avoid both underfitting and overfitting.
目标是找到平衡点，避免欠拟合和过拟合。
underfitting can be a consequence of insufficient training data or a poor feature set.
欠拟合可能是由于训练数据不足或特征集不佳造成的。
we used cross-validation to diagnose the extent of underfitting in the model.
我们使用交叉验证来诊断模型中欠拟合的程度。
underfitting risk
欠拟合风险
avoiding underfitting
避免欠拟合
susceptible to underfitting
易于欠拟合
underfitting problem
欠拟合问题
detecting underfitting
检测欠拟合
model underfitting
模型欠拟合
underfitting data
欠拟合数据
prevent underfitting
预防欠拟合
checking for underfitting
检查欠拟合
severe underfitting
严重的欠拟合
the model suffered from severe underfitting and failed to capture the underlying patterns.
模型存在严重的欠拟合现象，未能捕捉到潜在模式。
we noticed significant underfitting when evaluating the model on the test set.
在评估模型时，我们注意到明显的欠拟合现象。
underfitting often results from using a model that is too simple for the data.
欠拟合通常是由于使用过于简单的模型来处理数据所致。
to avoid underfitting, we increased the model complexity and added more features.
为了避免欠拟合，我们增加了模型的复杂性并添加了更多特征。
the linear regression model exhibited underfitting compared to the neural network.
与神经网络相比，线性回归模型表现出欠拟合的现象。
underfitting leads to poor performance on both training and test data.
欠拟合会导致训练数据和测试数据上的表现不佳。
we checked for underfitting by plotting the training and validation loss curves.
我们通过绘制训练和验证损失曲线来检查是否存在欠拟合。
regularization can sometimes exacerbate underfitting if applied too aggressively.
如果过度应用，正则化有时会加剧欠拟合的现象。
the goal is to find a balance and avoid both underfitting and overfitting.
目标是找到平衡点，避免欠拟合和过拟合。
underfitting can be a consequence of insufficient training data or a poor feature set.
欠拟合可能是由于训练数据不足或特征集不佳造成的。
we used cross-validation to diagnose the extent of underfitting in the model.
我们使用交叉验证来诊断模型中欠拟合的程度。
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