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Fill in the blanks for the following statement about overfitting in optimization using one of the following terms: more, less, training, validation, feature, data Overfitting do well on training validation data due to variance. The solutions for overfitting are: 1. More data powerful model. 3. Regularize your model data, but poorly on 2. less

Fill in the blanks for the following statement about overfitting in optimization using one of the following terms: more, less, training, validation, feature, data Overfitting do well on training validation data due to variance. The solutions for overfitting are: 1. More data powerful model. 3. Regularize your model data, but poorly on 2. less

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Fill in the blanks for the following statement about overfitting in optimization using one of the following terms: more, less, training, validation, feature, data Overfitting do well on training validation data due to variance. The solutions for overfitting are: 1. More data powerful model. 3. Regularize your model data, but poorly on 2. less
Transcribed Image Text:Fill in the blanks for the following statement about overfitting in optimization using one of the following terms: more, less, training, validation, feature, data Overfitting do well on training validation data due to variance. The solutions for overfitting are: 1. More data powerful model. 3. Regularize your model data, but poorly on 2. less
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