Which of the following is a technique for preventing overfitting in a supervised learning model?Review LaterAdding more hidden layers to a neural networkIncreasing the learning rate of a modelRegularizationUsing a larger batch size during training
Question
Which of the following is a technique for preventing overfitting in a supervised learning model?Review LaterAdding more hidden layers to a neural networkIncreasing the learning rate of a modelRegularizationUsing a larger batch size during training
Solution
The technique for preventing overfitting in a supervised learning model among the options provided is Regularization.
Here's a step-by-step explanation:
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Overfitting occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model.
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Regularization is a technique used to prevent overfitting in a supervised learning model. It does this by adding a penalty term to the loss function. The penalty term discourages complex models, i.e., models with large coefficient values.
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Adding more hidden layers to a neural network, increasing the learning rate of a model, or using a larger batch size during training are not specifically techniques for preventing overfitting. They are more related to the architecture and training speed of
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Which technique can help reduce overfitting in machine learning models? Increasing model complexity Decreasing the amount of training data Regularization Ignoring feature importance
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A common technique to reduce overfitting in neural networks is to apply ______________, which randomly drops units from the neural network during training.
6. Which of the following can we use to solve the problem of “overfitting” in a neural network?我們可以使用以下哪項解決神經網路中的「過度擬合」問題？Regularization 正則項／懲罰項Activation function 激勵函數Epoch 訓練次數All of the above options 以上選項皆可
Which of the following is NOT a typical method to improve an overfitting machine learning model?Add more dataSelect more featuresSelect a simpler algorithmImprove feature engineering
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