1. What can we infer about our kNN model when the value of K is too big? The model will capture a lot of noise as a result of overfitting. The training accuracy will be high, while the out-of-sample accuracy will be low. The model is overly generalized and underfitted to the data. The model will be too complex and not interpretable.

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1. What can we infer about our kNN model when the value of K is too big? The model will capture a lot of noise as a result of overfitting. The training accuracy will be high, while the out-of-sample accuracy will be low. The model is overly generalized and underfitted to the data. The model will be too complex and not interpretable.

Answer 1

The correct answer is: The training accuracy will be high, while the out-of-sample accuracy will be low.When the value of K in kNN (k-Nearest Neighbors) is too big, it means that the model is trying to find the k nearest neighbors for a given data point. If K is too large, the model will capture a lot of noise as a result of overfitting. This means that the model will fit the training data too closely and will not generalize well to new data. As a result, the training accuracy will be high, but the out-of-sample accuracy will be low.

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