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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.

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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.

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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Elit · 8 yıl öğretmeni
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The correct answer is: The training accuracy will be high, while the out-of-sample accuracy will be low.<br /><br />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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