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NEW QUESTION: 1
You are evaluating a Python NumPy array that contains six data points defined as follows:
data = [10, 20, 30, 40, 50, 60]
You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
You need to implement a cross-validation to generate the output.
How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: k-fold
Box 2: 3
K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).
The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.
Box 3: data
Example: Example:
>>>
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
References:
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html
NEW QUESTION: 2
決定木アルゴリズムを使用しています。次のツリー深度で一般化するモデルを訓練しました。
10。
さまざまなツリー深度値を持つモデルのバイアスおよび分散プロパティを選択する必要があります。
各ツリーの深さに対してどのプロパティを選択する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
Answer:
Explanation:
説明
In decision trees, the depth of the tree determines the variance. A complicated decision tree (e.g. deep) has low bias and high variance.
Note: In statistics and machine learning, the bias-variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa. Increasing the bias will decrease the variance. Increasing the variance will decrease the bias.
参照：
https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/
NEW QUESTION: 3
Refer to the Exhibit.
An administrator is installing Windows into a virtual machine. The DVD has been mounted on the Host and configured for the virtual machine as shown in the Exhibit.
Based on the exhibit, when the virtual machine is booted, why would it attempt to search for a PXE server?
A. The CD/DVD device is not set to Client Device.
B. The ISO is in the incorrect storage location.
C. The OS minimum requirements have not been met.
D. The CD/DVD device is not connected.
Answer: D
Explanation:
Explanation/Reference:
Explanation:
It looks like the CD/DVD device is not connected. Restart the machine and recheck again. When the system doesn't find CD/DVD drive, it looks for PXE server to boot.
Reference:
NEW QUESTION: 4
A. Option A
B. Option C
C. Option D
D. Option B
Answer: B