- In Python 1 (62.16 KiB) Viewed 30 times
In Python:
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answerhappygod
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In Python:
In Python:
1. Load any one of the toy/real datasets (for classification) from scikit-learn library. Display general information and statistics about your selected dataset. (5 points) 2. Split the dataset into train and test sets. (5 points) 3. Implement a k-nearest neighbors classifier (k=3) for the training set. You are not allowed to use the scikit learn library for the k-nearest neighbors classifier. Show the result for the test set using the following metric: accuracy, precision, and recall. (10 points) 4. Split the training set into 5-fold for cross-validation. You are not allowed to use the scikit-learn library method for cross-validation. Calculate accuracy for each held-out set. Also, display the average accuracy for five folds. (10 points)
1. Load any one of the toy/real datasets (for classification) from scikit-learn library. Display general information and statistics about your selected dataset. (5 points) 2. Split the dataset into train and test sets. (5 points) 3. Implement a k-nearest neighbors classifier (k=3) for the training set. You are not allowed to use the scikit learn library for the k-nearest neighbors classifier. Show the result for the test set using the following metric: accuracy, precision, and recall. (10 points) 4. Split the training set into 5-fold for cross-validation. You are not allowed to use the scikit-learn library method for cross-validation. Calculate accuracy for each held-out set. Also, display the average accuracy for five folds. (10 points)
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