The correct answer is: A. by using a lot of data
Overfitting is a common problem in machine learning, where a model learns the training data too well and does not generalize well to new data. This can happen when the model is trained on a small amount of data, or when the data is noisy or biased.
One way to avoid overfitting is to use a lot of data. This will help the model to learn the underlying patterns in the data, and to generalize better to new data.
Another way to avoid overfitting is to use regularization techniques. Regularization techniques add a penalty to the model’s loss function, which discourages the model from learning the training data too well.
Finally, it is important to evaluate the model on held-out data, also known as validation data. This data is not used to train the model, but is used to assess the model’s performance on new data. This can help to identify overfitting, and to prevent the model from being overfit to the training data.
Option B is incorrect because inductive machine learning is a type of machine learning that learns from a set of examples. This does not necessarily mean that inductive machine learning will avoid overfitting.
Option C is incorrect because using validation only will not prevent overfitting. The model should be evaluated on held-out data, also known as validation data. This data is not used to train the model, but is used to assess the model’s performance on new data. This can help to identify overfitting, and to prevent the model from being overfit to the training data.