the model selection process requires careful consideration of multiple factors.
模型选择过程需要仔细考虑多个因素。
we use specific model selection criteria to evaluate different algorithms.
我们使用特定的模型选择标准来评估不同算法。
the model selection method depends on the dataset characteristics.
模型选择方法取决于数据集的特征。
automated model selection can save time in machine learning projects.
自动化模型选择可以在机器学习项目中节省时间。
cross-validation is essential for effective model selection.
交叉验证对于有效的模型选择至关重要。
the model selection algorithm compares performance metrics.
模型选择算法会比较性能指标。
our framework includes a robust model selection procedure.
我们的框架包含一个稳健的模型选择程序。
the model selection strategy should align with project goals.
模型选择策略应与项目目标一致。
statistical tests inform the model selection technique.
统计检验为模型选择技术提供信息。
the model selection approach varies by application domain.
模型选择方法因应用领域而异。
proper model selection criteria prevent overfitting.
适当的模型选择标准可以防止过拟合。
the model selection phase determines the best predictor.
模型选择阶段决定了最佳预测器。
machine learning practitioners rely on model selection best practices.
机器学习从业者依赖于模型选择的最佳实践。
the model selection process requires careful consideration of multiple factors.
模型选择过程需要仔细考虑多个因素。
we use specific model selection criteria to evaluate different algorithms.
我们使用特定的模型选择标准来评估不同算法。
the model selection method depends on the dataset characteristics.
模型选择方法取决于数据集的特征。
automated model selection can save time in machine learning projects.
自动化模型选择可以在机器学习项目中节省时间。
cross-validation is essential for effective model selection.
交叉验证对于有效的模型选择至关重要。
the model selection algorithm compares performance metrics.
模型选择算法会比较性能指标。
our framework includes a robust model selection procedure.
我们的框架包含一个稳健的模型选择程序。
the model selection strategy should align with project goals.
模型选择策略应与项目目标一致。
statistical tests inform the model selection technique.
统计检验为模型选择技术提供信息。
the model selection approach varies by application domain.
模型选择方法因应用领域而异。
proper model selection criteria prevent overfitting.
适当的模型选择标准可以防止过拟合。
the model selection phase determines the best predictor.
模型选择阶段决定了最佳预测器。
machine learning practitioners rely on model selection best practices.
机器学习从业者依赖于模型选择的最佳实践。
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